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Brazilian Journal of Business 1
ISSN: 2596-1934
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Technological unemployment in terms of global labor market imbalances:
bibliometric analysis
Desemprego tecnológico em termos de desequilíbrios no mercado de trabalho
global: análise bibliométrica
El desempleo tecnológico en términos de desequilibrios mundiales del mercado
laboral: análisis bibliométrico
DOI: 10.34140/bjbv7n1-039
Submetido: 02/12/2024
Aprovado: 15/01/2025
Souheyla Cherif
Ph.D. candidate
Laboratory of MQEMADD; University of Djelfa
Djelfa. Algeria
souheyla.cherif@univ-djelfa.dz
Siham Gourida
Professor
Laboratory of MQEMADD; University of Djelfa
Djelfa. Algeria
siham.gourida@univ-djelfa.dz
ABSTRACT
The current society, known as the supersmart society or Society 5.0, emerged in response to the Fourth
Industrial Revolution, also known as Industry 4.0 (I4.0). This revolution arose with the development of
technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML),
and Big Data Analytics (BDA). According to the Future of Jobs Report 2023, the most significant macro
trend driving business transformation is the increased adoption of frontier technologies, with an 86.2%
rate. I4.0 has reshaped production and manufacturing, transforming firms into smart factories. One
consequence of I4.0 is technological unemployment (TU).
This paper evaluates the literature on I4.0 and TU from 2015 to 2024, focusing on labor market imbalances
(LMI). Through bibliometric analysis, it assesses publication and citation performance across authors,
institutions, countries, and journals. Additionally, it examines bibliometric citation trends, including cited
references.
To achieve this, we adopted a quantitative approach and a confirmatory-explicative research method to
analyze TU within I4.0 transformations and its impact on LMI. Bibliometric analysis was used to identify
data patterns, trends, and relationships in the literature. Our findings reveal significant trends, notably job
polarization, income inequality, and TU. However, the effects of I4.0 on employment are not universally
negative; in some cases, technological investments create new professions and job opportunities.
Occupations requiring human judgment, decision-making, creativity, and innovation show resilience to
technological advancements.
Keywords: technological unemployment, labor market imbalances, Industry 4.0, performance analysis,
bibliometric analysis.
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RESUMO
A sociedade atual, conhecida como sociedade superinteligente ou Sociedade 5.0, surgiu em resposta à
Quarta Revolução Industrial, também conhecida como Indústria 4.0 (I4.0). Essa revolução ocorreu com o
desenvolvimento de tecnologias como a Internet das Coisas (IoT), Inteligência Artificial (IA),
Aprendizado de Máquina (ML) e Análise de Big Data (BDA). De acordo com o ‘Future of Jobs Report
2023’, a principal macrotendência que impulsiona a transformação dos negócios é a crescente adoção de
tecnologias de ponta, com uma taxa de 86,2%. A I4.0 remodelou os processos produtivos e a manufatura,
transformando as empresas em fábricas inteligentes. Uma das consequências da I4.0 é o desemprego
tecnológico (TU).
Este artigo avalia a literatura sobre I4.0 e TU no período de 2015 a 2024, com foco nos desequilíbrios do
mercado de trabalho (LMI). Por meio da análise bibliométrica, examina o desempenho de publicações e
citações de autores, instituições, países e periódicos. Além disso, analisa tendências de citações
bibliométricas, incluindo referências citadas. Para isso, adotamos uma abordagem quantitativa e um
método de pesquisa confirmatório-explicativo para analisar o TU no contexto das transformações da I4.0
e seu impacto no LMI. A análise bibliométrica foi utilizada para identificar padrões, tendências e relações
nos dados da literatura. Nossos resultados revelam tendências significativas, como a polarização do
emprego, a desigualdade de renda e o TU. No entanto, os efeitos da I4.0 no emprego não são
universalmente negativos; em alguns casos, investimentos tecnológicos criam novas profissões e
oportunidades de trabalho. Ocupações que exigem julgamento humano, tomada de decisão, criatividade e
inovação demonstram resiliência aos avanços tecnológicos.
Palavras-chave: desemprego tecnologico, desequilibrios no mercado de trabalho, Industria 4.0, analise
de desempenho, analise bibliometrica.
RESUMEN
La sociedad actual, conocida como la sociedad superinteligente o Sociedad 5.0, surgió como respuesta a
la Cuarta Revolución Industrial, también conocida como Industria 4.0 (I4.0). Esta revolución se produjo
con el desarrollo de tecnologías como el Internet de las Cosas (IoT), la Inteligencia Artificial (IA), el
Aprendizaje Automático (ML) y el Big Data Analytics (BDA). Según el Informe sobre el Futuro del
Empleo 2023, la principal macrotendencia que impulsa la transformación empresarial es la creciente
adopción de tecnologías de vanguardia, con una tasa del 86,2%. La I4.0 ha remodelado los procesos de
producción y fabricación, convirtiendo a las empresas en fábricas inteligentes. Una de las consecuencias
de la I4.0 es el desempleo tecnológico (TU).
Este artículo evalúa la bibliografía sobre la I4.0 y el desempleo tecnológico desde 2015 hasta 2024,
centrándose en los desequilibrios del mercado laboral. Mediante un análisis bibliométrico, examina el
rendimiento de las publicaciones y las citas de autores, instituciones, países y revistas. También analiza
las tendencias bibliométricas de las citas, incluidas las referencias citadas. Para ello, adoptamos un
enfoque cuantitativo y un método de investigación confirmatorio-explicativo para analizar el UW en el
contexto de las transformaciones I4.0 y su impacto en el LMI. Se utilizó el análisis bibliométrico para
identificar patrones, tendencias y relaciones en los datos bibliográficos. Nuestros resultados revelan
tendencias significativas, como la polarización del empleo, la desigualdad de ingresos y la IU. Sin
embargo, los efectos de la I4.0 sobre el empleo no son universalmente negativos; en algunos casos, las
inversiones tecnológicas crean nuevas profesiones y oportunidades de empleo. Las ocupaciones que
requieren criterio humano, toma de decisiones, creatividad e innovación demuestran resistencia a los
avances tecnológicos.
Palabras clave: desempleo tecnológico, desequilibrios del mercado laboral, Industria 4.0, análisis del
rendimiento, análisis bibliométrico.
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1 INTRODUCTION
Industry 4.0, driven by rapid advancements in robotics and digitalization, is reshaping global
manufacturing systems and labor markets (Liboni et al., 2019; Strandhagen et al., 2017). Businesses face
significant challenges and opportunities as they adapt to automation, cyber-physical systems, and artificial
intelligence, with job automation becoming a central concern for human resources management (Stachová
et al., 2019). While innovative companies can benefit from these changes, those unable to adapt may
struggle. The transition demands both acquiring new talent with advanced digital skills and upskilling
existing employees, as vocational and academic training plays a crucial role in Industry 4.0
implementation (Stachová et al., 2019). However, the abrupt shift to full automation is hindered by HRM-
related issues, such as the lack of skilled workers and high labor turnover due to rapid technological
progress (Liboni et al., 2019; Postel‐Vinay, 2002). Schumpeter’s model of economic growth predicts that
fast technological advancements can lead to job destruction, requiring businesses to balance job losses
with the creation of new employment opportunities (Postel‐Vinay, 2002).
Technological progress is accelerating the transition from mass production economies to digital
services, significantly influencing labor market dynamics (Bertani et al., 2020). Automation and AI are
contributing to job displacement and labor market polarization, leading to employment growth primarily
at the extremes of the income spectrum (McGuinness et al., 2023). Predictions indicate that automation
will impact millions of workers by 2030, with technological unemployment becoming a growing concern
(Bertani et al., 2020). Despite an anticipated economic slowdown in 2023, the International Federation of
Robotics forecasts continued growth in robot installations, reaching 700,000 units by 2026. This ongoing
shift highlights the need for businesses and policymakers to develop strategies that mitigate labor market
imbalances caused by digitalization and automation. This study aims to conduct a bibliographic analysis
to assess Industry 4.0’s role in labor market shifts and technological unemployment worldwide.
2 LITERATURE REVIEW
2.1 INDUSTRY 4.0
The history of mankind in terms of social evolution consists of five different societies (1-the
hunting society; 2-the agricultural society; 3-the industrial society; 4-the information society; 5-and the
supersmart society (Society 5.0), which is currently emerging with Industry 4.0) (Kurt, 2019). The use of
steam power in the 18th century led to a shift from human labor to the power of the machine, resulting in
lower production costs and the sale of standardized, high-quality goods. Industry 2.0 and 3.0 were the
application of electricity and information technologies (Lasi et al., 2014; Li et al., 2017), while Industry
4.0 emerged with the addition of Internet of Things (IoT), Cyber-Physical Systems (CPS) and more (e.g.,
Cloud computing, Big data analytics, AI and machine learning, Cyber security, Digital twin/ stimulation,
Augmented reality (AR), Additive manufacturing/3D printing, Autonomous robots) (Kerin & Pham,
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2019). 2011's Hannover Fair marked the debut of the phrase "Industry 4.0” when Robert Bosch and
Kagermann formed a working group to propose the fourth industrial revolution to the German Federal
Government (Kurt, 2019; Sony & Naik, 2019). The I4.0 is now referred to in different ways, such as
Society 5.0 in Japan, Industry 4.0 in Germany and Turkey, and the Internet of Things in the US.
I4.0 describes various modifications to manufacturing systems that are primarily IT-driven (Lasi
et al., 2014). Such as the progress in gene sequencing, nanotechnology, artificial intelligence, and
renewable energy sources (Kurt, 2019). Therefore, I4.0 refers to the process of transforming organizations
into smart factories. By using a wide range of technologies (Sony & Naik, 2019), These changes are also
referred to as the Internet of Things (IoT), Internet of Services (IoS), Internet of People (IoP) (Zezulka et
al., 2016), and internet of energy (IoE) (Khan et al., 2017). In addition, I4.0 significantly affects the nature
of work, the identity of employees, and the relationship between employees and employers due to the
intensive human-machine interaction (Kurt, 2019). These changes in the labor market and production
processes are reducing the numbers of employed workers especially those in the working class (Kurt,
2019), resulting in large unemployment rates (Liboni et al., 2019).
2.2 TECHNOLOGICAL UNEMPLOYMENT
Industry 4.0 significantly affects the nature of work, the identity of employees, and the relationship
between employees and employers due to the intensive human-machine interaction (Kurt, 2019). This
struggle between humans and machines for knowledge-intensive jobs is one facet of moving toward
complete automation (Liboni et al., 2019). Instead of serving the sole purpose of aiding humans, factory
robots are actively participating in the workforce and working teams. Therefore, it is crucial to create
homogeneity between humans and robots (Kurt, 2019) to face the technological unemployment threats.
Robotics are widely used in many industries, particularly manufacturing, resulting in
"technological unemployment," which is the term used to describe how I4.0 has affected the workforce
by changing the employment landscape and employment models (Kurt, 2019). Consequently, many
professions will disappear or become less prevalent, while others will appear due to the specialization in
the I4.0 revolution. These changes in the labor market and production processes are reducing the numbers
of employed workers especially those in the working class (Kurt, 2019), resulting in large unemployment
rates (Liboni et al., 2019).
Technological unemployment is a negative consequence of automation (Lima et al., 2021), and the
level of this latter is influenced by the digitalization strategy of each nation and the speed of its
implementation, as well as the readiness of a particular country's education system to retrain susceptible
groups in the labor market (Szabó-Szentgróti et al., 2021). Therefore, technological unemployment
degrees differ from one country to another, and from small towns to large cities. Considering technological
unemployment as a long-term economic shift, workers need to acquire new skills to meet the new
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occupations’ needs (Lima et al., 2021). As mentioned by Autor (2022), At the top of the labor market, an
increasing number of high-education, high-wage jobs provide promising career paths, rising lifetime
wages, and considerable job stability. On the other hand, low-education, low-wage industries, particularly
in personal services, provide minimal economic stability and restricted career salary development.
3 RESEARCH METHODOLOGY
3.1 RESEARCH OBJECTIVES AND QUESTIONS
This study intends to conduct an in-depth review of the literature on I4.0 and TU and to explore
their impacts on the LMI. To achieve this, we have established the following research questions:
RQ1: how does the impact of I4.0 on the labor market differ from one country to another?
RQ2: what are the intrinsic changes that occurred to the labor market in response to technological
progress in the period of (2015-2024)?
RQ3: what are the types of occupations that remain resilient in the face of technological
advancements?
RQ4: on what basics does technological unemployment vary from one sector to another?
RQ5: what are the anticipated impacts of technological unemployment on the labor market
imbalances in the future?
RQ6: what are the possible positive aspects of Industry 4.0 on the labor market?
3.2 DATA COLLECTION
According to Noyons (2001), the most frequently analyzed elements of a bibliographic record
include the authors, their affiliations, keywords, the year of publication, and the source or journal where
the document is published. Our data is keyword-based, the following table represents the keyword groups
used to search for the articles related to our topic from the Dimensions database:
Table 1: keyword groups used in the research
Groups
Keywords
Publication Years
Dimensions Database
Grp 1
("industry 4.0" AND "technological
unemployment")
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
92
Grp 2
("industry 4.0" AND "labor market")
54
Grp 3
("indu stry 4.0" AND "technological
unemployment" AND "labor market")
68
Grp 4
("labor market" AND "technological
unemployment")
231
Grp 5
(("industry 4.0" OR "technological
advancement" OR "technological progress")
AND ("technological unemployment" OR
"unemployment") AND ("labor market" OR
"labor market imbalances"))
389
Total
/
/
834
Source: Adapted from Dimensions database
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The dimensions database is used in this study to collect bibliographic components about “Industry
4.0, technological unemployment, and labor market imbalances”. The dimensions database consists of
more than 137 million publications with 209000 source titles (journals, book series, preprint servers,
conference proceedings), which provides wide-ranging access to bibliographic and citation information.
The table above demonstrates the data we found in the period of (2015-2024) that are mostly related to
our topic, the total number of these publications is 834. After merging the duplicated files using Zotero
software and deleting the ones written in languages other than English, we found 637. Table 02 details the
inclusion criteria used to filter and select the most pertinent research papers from the Dimensions database.
Table 2: Inclusion Criteria
Search Type
Publication
Years
Publication
Type
Fields of Research
Journal List
Grp 1
Full data
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
Article, Edited
Book, Chapter
Commerce, Management, Tourism
and Services Strategy,
Management and Organisational
Behaviour
Human Resources and Industrial
Relations
DOAH
ERIH PLUS
ERA 2023
Grp 2
Title and
Abstract
Article, Edited
Book, Chapter
Commerce, Management, Tourism
and Services Strategy,
Management and Organisational
Behaviour
Human Resources and Industrial
Relations
DOAH
ERIH PLUS
ERA 2023
Grp 3
Full Data
Article, Edited
Book, Chapter
Commerce, Management, Tourism
and Services Strategy,
Management and Organisational
Behaviour
Human Resources and Industrial
Relations
DOAH
ERIH PLUS
ERA 2023
Grp 4
Full Data
Article, Edited
Book, Chapter
Commerce, Management, Tourism
and Services Strategy,
Management and Organisational
Behaviour
Human Resources and Industrial
Relations
DOAH
ERIH PLUS
ERA 2023
Grp 5
Full Data
Article
Human Resources and Industrial
Relations
DOAH
ERIH PLUS
ERA 2023
Source: Adapted from Dimensions database
3.3 BIBLIOMETRIC ANALYSIS
The subsequent phase of our research involves evaluating the content of the gathered research by
reviewing the titles and abstracts to confirm if the database has identified relevant articles for our analysis.
Consequently, we omitted (197) articles deemed irrelevant to our research problem or have been repeated.
For the final analysis, the authors carefully examined the complete papers (637) and chose 28 articles for
inclusion in the qualitative assessment. For a comprehensive understanding of our methodological
approach, please see Fig 01.
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Figure 1: Methodological approach.
Source: Prepared by researchers
following the work of Alon et al. (2018), we utilized analytical visualization software (VOSviewer)
to identify the citation linkages of the 637 identified papers. VOSviewer is a tool of data analytics that can
be used to analyze and visualize direct and indirect citation relationships between articles by simply
revealing who cited whom (Pasadeos et al., 1998).
4 RESULTS
4.1 MOST INFLUENTIAL PAPERS IN THE LITERATURE OF “INDUSTRY 4.0,
TECHNOLOGICAL UNEMPLOYMENT, AND LABOR MARKET IMBALANCES”
In this part, we conducted a performance analysis, by evaluating the “publication” and “citation”
performances of authors, institutions/universities, countries, and journals (Öztürk et al., 2024) to
determine whether our dataset is effective in the relevant field or not. To set the scientific value of the
papers, scholars have used various bibliographic analyses, for instance, the methodology of Alon et al.
(2018) was to identify the total global citation (TGC) and the total local citation (TLC) of their sample.
TGC indicates how often an article has been cited according to the full count in the ISI Web of Science
database. This number also highlights the interdisciplinary nature of the research paper and its overall
impact on academic research. While TLC implies the number of citations an article gets within the same
literature set. On the other side, Szabó-Szentgróti et al. (2021) assessed the scientific value of studies by
the published journals' rankings. These rankings indicate a journal's quartile within its field, where this
international ranking system classifies journals into four categories “Q1, Q2, Q3, and Q4”.
4.1.1 Institutions and Universities
Most technological advancements in theorization have started in developed countries and stem
from revolutionized industries. Much of the work on Industry 4.0 seeks to determine its impact on the
labor market and technological unemployment. Therefore, we started our investigation by examining the
institutions associated with our sample papers and their countries of origin. Precisely, we aim to analyze
the impact of different research by different countries. The table below demonstrates the most influential
institutions regarding citation numbers (e.g., Old Dominion University 809; University of Colorado
Boulder 561; University of Colorado Denver 561; Catholic University of the Sacred Heart 491…etc).
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Table 3: Most influential institutions.
Organizations
Documents
Citations
Citation per document
Auckland University of Technology
4
470
117.5
Catholic University of the Sacred Heart
12
491
40.9
Institute for the Study of Labor
10
366
36.6
Institute of Economics Agriculture, Volgina,
Belgrade, Serbia
1
376
376
Massey University
6
400
66.6
Old Dominion University
2
809
404.5
Ovidius University
1
376
376
Petroleum & Gas University of Ploieşti
1
376
376
Sant'anna School of Advanced Studies
6
289
48.2
United Nations University – Maastricht
Economic and Social Research Institute on
Innovation and Technology
6
329
54.8
University of Colorado Boulder
1
561
561
University of Colorado Denver
1
561
561
Source: adapted from the VOSviewer software
When compiling institutional contributions to Industry 4.0 nationally (see Table 04), the United
States had the highest number of contributions (88 papers), followed by the United Kingdom (60), Italy
(52), China (42), Germany (41), Australia (35), India (31), Poland (27), and finally the Netherlands and
Spain with (25papers) for each. When evaluating the quality of contributions based on citations, the United
States is found to have the greatest impact on this literature set with a total number of citations of (3417),
followed by the United Kingdom (1370), Italy (1301), Germany (1205), and Netherlands (1004). Our
research reveals a diverse range of institutional backgrounds driving the advancement of the Industry 4.0
literature. While there's notably a greater number of contributions from institutions in Western nations
compared to developing countries as mentioned earlier, the significant influence of literature from
institutions in developing countries in terms of quantity and quality, is noteworthy. Table 05 presents all
the developing countries (revised from WorldData.info) that contributed to our literature set.
Table 4: Most influential international contributions.
Country
Documents
Citations
Australia
35
959
China
42
583
Germany
41
1205
India
31
317
Italy
52
1301
Netherlands
25
1004
Poland
27
267
Spain
25
335
United Kingdom
60
1370
United States
88
3417
Source: adapted from the VOSviewer software
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Table 5: The contributions from developing countries.
Country
Documents
Citations
Azerbaijan
1
0
Bahrain
1
17
Bangladesh
3
22
brazil
16
396
Bulgaria
3
86
Chile
3
14
China
42
583
costa rica
1
9
Ecuador
1
0
Egypt
1
0
Georgia
1
0
Ghana
3
0
India
31
317
Indonesia
5
0
Iran
1
0
Kazakhstan
2
5
Lebanon
1
0
Malaysia
12
76
Mexico
2
1
Morocco
1
4
Namibia
1
19
Nepal
2
0
Nigeria
3
25
Pakistan
3
31
Philippines
2
34
Poland
27
267
Romania
13
515
Russia
9
74
Saudi Arabia
4
20
Serbia
2
6
South Africa
15
190
Thailand
1
28
Turkey
8
126
Ukraine
12
45
Vietnam
1
23
Total
234
2933
Source: adapted from the VOSviewer software
4.1.2 Influential Journals
Among the 336 sources included in our research sample, six journals were found to publish 10 or
more articles related to Industry 4.0 and technological unemployment during the period of (2015-2024)
(refer to Table 06). In terms of journals’ published papers quality, 12 journals surpassed the threshold of
200 citations (see Table 07). When merging both criteria of the number of documents and citations of each
journal, we found that five journals have the most influence over the literature of industry 4.0 and
technological unemployment, which are IJERPH (15D., 349C.), IJM (17D., 206C.), NTWE (12D.,
504C.), Sustainability (32D., 889C.), and TFSC (15D., 1351C.). Our analysis indicates that the
sustainability journal is the most influential in the industry 4.0 and technological unemployment studies
due to its high focus (Documents=32), while TFSC has the highest impact on this literature set
(Citations=1351).
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Table 6: Most influential journals in terms of published documents (>=10 documents).
Source
Abbreviations
Documents
Citations
International Journal of Environmental Research and Public
Health
IJERPH
15
349
International Journal of Manpower
IJM
17
206
International Labour Review
ILR
14
109
New Technology Work and Employment
NTWE
12
504
Sustainability
-
32
889
Technological Forecasting and Social Change
TFSC
15
1351
Source: adapted from the VOSviewer software
Table 7: Most influential journals in terms of document quality (>= 200 citations).
Source
Abbreviations
Documents
Citations
Annual Review of Organizational Psychology and Organizational
Behavior
1
561
International Journal of Environmental Research and Public
Health
IJERPH
15
349
International Journal of Manpower
IJM
17
206
Journal of Economic Surveys
JES
4
300
Journal of Management & Organization
JMO
1
343
Journal of Vocational Behavior
JVB
5
306
New Technology Work and Employment
NTWE
12
504
Research Policy
RP
9
326
Sustainability
-
32
889
Technological Forecasting and Social Change
TFSC
15
1351
The Economic and Labour Relations Review
TELRR
4
220
Work and Occupations
WO
4
255
Source: adapted from the VOSviewer software
4.1.3 Influential Research Papers
It's crucial to identify which work has most significantly influenced the I4.0 and TU literature to
understand how this field of research has developed. For this purpose, we aimed to understand the citation
trends to provide the most prominent articles that shaped this literature set. Table 08 provides an overview
of the 10 articles that had the strongest impact on the I4.0 and TU literature. The most influential five
papers in the determined period (2015-2024) are Li (2018), followed by Cascio (2016), Sima (2020),
Brougham (2017), and Stanford (2017).
Table 8: The most influential articles in the I4.0 and employment.
Citations
Article Title
Stanford (2017)
196
The resurgence of gig work: Historical and theoretical perspectives
Pfeiffer (2016)
155
Robots, Industry 4.0 and Humans, or Why Assembly Work Is More than Routine
Work
Brougham (2017)
343
Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA):
Employees’ perceptions of our future workplace
Dachs (2019)
157
Bringing it all back home? Backshoring of manufacturing activities and the adoption
of Industry 4.0 technologies
Cascio (2016)
561
How Technology Is Changing Work and Organizations
Dengler (2018)
193
The impacts of digital transformation on the labor market: Substitution potentials of
occupations in Germany
Sima (2020)
376
Influences of the Industry 4.0 Revolution on Human Capital Development and
Consumer Behavior: A Systematic Review
Fleming (2018)
177
Robots and Organization Studies: Why Robots Might Not Want to Steal Your Job
Liboni (2019)
176
Smart industry and the pathways to HRM 4.0: implications for SCM
Li (2018)
770
China's manufacturing locus in 2025: With a comparison of “Made-in-China 2025”
and “Industry 4.0”
Source: adapted from the VOSviewer software
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4.2 VISUALIZATION AND EXAMINATION OF BIBLIOMETRIC CITATION OUTCOMES
In this section, we used data analytics visualization and co-citation mapping to analyze our sample
articles. Co-citation mapping provides insights into the evolution of a research field by highlighting key
concepts from the most cited papers. This method does more than measure popularity; it also identifies
the development, main theories, and key topics within a field. Citation analysis helps track publishing
patterns and reveals which publications and authors are frequently cited together (Alon et al., 2018).
Therefore, we can understand the foundational and emerging trends in I4.0, TU, and LM research by
focusing on co-citation. To effectively examine co-citation networks in our sample, we included only
articles cited at least 16 times between 2015 and 2024. This criterion helps us focus on the relationships
and impact of the most influential articles. Based on this threshold, our subsample consists of 28 articles.
Following the co-citation mapping of the referenced sources, a systematic content analysis was
carried out. This method provides valuable insights by uncovering key research streams (Alon et al.,
2018). Additionally, it plays a crucial role in advancing knowledge within a given research field (Gaur &
Kumar, 2018). Many scholars emphasize that content analysis results are more credible and reliable when
performed collaboratively by multiple researchers (Alon et al., 2018). Therefore, two researchers
conducted a structured content analysis of the 28 most frequently cited articles.
Figure 2: Density visualization
Source: adapted from the VOSviewer software
4.2.1 The Cross Relationship between Industry 4.0 and Unemployment
Industry 4.0 technologies, characterized by advancements in artificial intelligence, robotics,
nanotechnologies, 3D printing, and bioengineering, are transforming industries (Calvino & Virgillito,
2018), and impacting employment by creating jobs in new sectors while rendering some traditional roles
obsolete (Hall, 2001), potentially leading to technological unemployment (Bogliacino et al., 2012). Robots
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and automation technologies generally displace workers from tasks they used to perform, leading to
distinct labor market effects compared to other technological changes (Acemog & Restrepo, 2019). As
predicted by previous scholars (e.g., Acemoglu & Restrepo, 2019), labor became less important and the
advances in automation and artificial intelligence led to massive job losses. Many scholars predicted that
technologies will enable the automation of a significant portion of jobs within the next twenty years.
Furthermore, they believed that this automation will not be limited to the jobs most susceptible in the near
term but will eventually extend to jobs that currently seem secure from automation (Arntz et al., 2017).
I4.0 and unemployment are interconnected as automation and robotics adoption increase in response
to several changes like demographic (e.g., an aging population) (Acemoglu & Restrepo, 2017), or financial
(e.g., decreasing cost of computing and the substitution of labor with computer capital, driven by
advancements in Machine Learning (ML) and Mobile Robotics (MR)) (Frey & Osborne, 2017). This
adoption helps neutralize or reverse the negative effects of labor scarcity (Acemoglu & Restrepo, 2017).
However, I4.0 reduced the demand for routine-intensive jobs due to routine-biased technological change
(RBTC), which replaces labor in routine tasks. Leading to higher unemployment in routine-heavy sectors
while increasing jobs in high-skill and low-skill occupations, thus causing job polarization (Acemoglu &
Autor, 2011; Goos et al., 2014; Frey & Osborne, 2017; Acemoglu & Restrepo, 2018), and leading to labor
market polarization in advanced economies like the US, Japan, and the EU (Acemoglu & Autor, 2011).
Therefore, I4.0 has heightened the risk of job displacement due to automation, particularly among lower-
skilled workers. This risk is most acute in private sector jobs that do not provide sufficient remedial
training, underscoring the necessity for stronger lifelong learning policies to mitigate unemployment risks
associated with technological advancements (Pouliakas, 2018)
Technological progress, particularly skill-biased and routine-biased technological change, has
resulted in wage inequality and job polarization, with middle- and low-skilled jobs being the most affected
(Bogliacino et al., 2012). Nevertheless, technological advancements raised concerns about the potential
for widespread technological unemployment even among high-skilled workers. For instance,
advancements in prediction technology, a core component of Industry 4.0, can lead to the substitution of
labor with capital, particularly in tasks heavily reliant on prediction. For example, AI is increasingly
replacing human roles in demand forecasting and human resources tasks such as recruiting, promotion,
and retention. The automation of prediction can also enhance the returns to automating decision tasks,
thereby reducing the need for human intervention (Agrawal et al., 2019).
The Industrial Revolution saw increased income inequality, a trend that continued with Industry 4.0,
as wage gains became more uneven across different regions and job types (Mokyr et al., 2015), due to the
job polarization, where wage gains are most significant for high-skill and low-skill jobs, but less so for
middle-skill jobs (Autor, 2015). Moreover, Spencer (2018) demonstrated that the threat from technology
is seen not only in the loss of work but also in the degradation of work quality, new technologies might
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result in more precarious, insecure, and low-paid work in services even as they automate more complex
tasks.
On the other hand, innovations related to I4.0 can positively impact employment. Calvino &
Virgillito (2018) emphasized that product innovation can create jobs by opening new markets, while
process innovation may displace labor by increasing efficiency. In other words, product innovations can
lead to increased demand and potentially more employment (Spencer, 2018), while process innovations
aim to improve productivity, often reducing the need for labor in the short term but possibly increasing
employment in the long term due to higher production levels (Lachenmaier & Rottmann, 2011; Harrison
et al., 2014; Bogliacino & Pianta, 2010). The overall impact on employment varies depending on factors
such as market conditions and the relative efficiency of producing new versus old products (Harrison et
al., 2014)
Graetz & Michaels (2018) mentioned variations in robot adoption across sectors, with transport
equipment and chemicals leading. Therefore, the impact of I4.0’s integration varies across countries and
sectors, indicating a complex relationship between technological advancements and employment
dynamics (Vivarelli, 2014; Graetz & Michaels, 2018). Because automation can substitute or complement
labor, which may result in higher productivity and demand for labor in certain areas even as it replaces
jobs in others. This nuanced interaction between automation and employment underlines the complexity
of Industry 4.0's impact on jobs (Autor, 2015). Acemoglu & Restrepo (2019) confirmed that shifts in the
task content of production negatively impact labor demand, and over the past three decades, adverse shifts
due to rapid automation have reduced labor demand.
To face the threats of I4.0, Arntz (2014) explained three existing mechanisms to offset the negative
effects of technological advances on labor-saving (Arntz et al., 2017). First of all, labor-saving
technologies need to be produced from the beginning to increase the labor demand by creating new
occupations and sectors; Moreover, new technologies can enhance a company's competitive edge by
boosting productivity. This increased productivity often leads to lower costs and prices, resulting in higher
demand for products and services and, consequently, increased demand for labor. This can partially offset
the labor-saving impact of technologies; Additionally, when new technologies complement workers, it
enhances labor productivity. This can result in higher wages, increased employment, or both, leading to
higher labor income. Consequently, these well-compensated workers may have greater purchasing power,
leading to increased demand for products and services, further driving up the demand for labor within the
economy.
The future of middle-skill jobs may entail a blend of technical tasks and interpersonal skills that
machines struggle to replicate, suggesting that Industry 4.0 could reshape but not eliminate these roles
(Autor, 2015). However, historical evidence and contemporary estimates challenge the idea of a future
post-work society. Economies equipped with adaptable and inclusive education systems, vocational
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training programs, and robust social security safety nets can adjust, reallocating displaced resources to
other value-creating industries over time (Arntz et al., 2017). Mokyr et al. (2015) noted that while
mechanization has replaced certain jobs, it has also spawned new roles like mechanics and engineers,
illustrating how Industry 4.0 can create fresh employment opportunities despite disrupting existing ones.
Hence, automation leads to labor displacement and still generates new tasks that can reintegrate labor,
thus affecting overall labor demand (Acemoglu & Restrepo, 2019). Ultimately, to navigate technological
shifts effectively, nations must adapt their institutional frameworks, including labor market regulations
and social policies, to support employment in emerging sectors while maintaining overall employment
levels (Hall, 2001).
4.2.2 Labor Market Implications Linked to the Digital Revolution
Over the last two decades, technological advances broadly affected employment trends. Their
implications can be divided into two groups, positive and negative. The positive ones are limitless such
as job creation (e.g., higher innovations are expected to positively impact employment based on the study
of Van Reenen (1997)), increased efficiency, and skill enhancement (e.g., the labor force in all
industrialized nations has experienced a significant relative growth in the percentage of workers with
advanced levels of education and this came as a response to the changes in the skills requirements (Spitz‐
Oener, 2006)). In contrast, the negative effects took a larger place in the literature, these effects are
mentioned below:
Job Polarization: The model of Autor et al. (2006) indicates that computerization enhances
nonroutine cognitive tasks, replaces routine tasks, and has little effect on non-routine manual tasks. This
technological shift accounts for labor market polarization, showing substantial growth in high-skilled jobs
requiring nonroutine cognitive tasks and a decrease in middle-skilled jobs focused on routine tasks.
Therefore, technological changes impact the labor market by altering the demand for different skill levels,
influencing the elasticity of substitution between high-skill and low-skill labor (Acemoglu & Autor, 2011;
Frey & Osborne, 2017). Moreover, Autor & Dorn (2013) and Autor (2015) confirmed that the employment
changes in the United States exhibit a pronounced U-shaped pattern by skill level, where the two upper
skill quartiles (high skills and low skills) increased and the median skill level decreased. In addition,
Acemoglu & Restrepo (2017) confirmed that job polarization led to significant employment shifts both
within industries (decline in routine tasks) and between industries (greater reductions in routine task-
intensive sectors).
Income Inequality: Autor et al. (2006) explored the changing wage structure in the U.S.,
highlighting key factors contributing to rising wage inequality. They identified skill-biased technical
change, which has increased the demand for educated workers (Autor et al., 2003), and a slowdown in the
growth of college-educated workers as primary drivers. Additionally, the weakening of labor market
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institutions, including the decline of unions and the reduced real value of the minimum wage, has further
exacerbated wage disparities. Since the 1990s, the labor market has become polarized, with job growth
mainly in high-wage and low-wage positions, reducing middle-skill employment. This polarization
contrasts with the 1980s, where job growth was more evenly spread across skill levels, leading to a
shrinking middle class and increased wage inequality (Autor et al., 2006; Autor & Dorn, 2013; Goos et
al., 2014). These points collectively illustrate how technological advancements and shifts in labor demand
have created labor market imbalances, increased wage inequality, and led to a polarized employment
structure (Autor et al., 2006; Goos et al., 2014).
Job Displacement: The introduction of AI and robotics is linked to the potential displacement of
human labor in many sectors (Spencer, 2018). High-skilled workers often benefit from technological
advances, while low-skilled workers may be displaced. The impact of innovation on employment also
depends on market dynamics, such as whether new products expand the market or simply shift market
share among existing firms (Harrison et al., 2014). For instance, product innovations tend to create jobs
by introducing new products, while process innovations can lead to labor displacement by improving
productivity. Conversely, the shift from manufacturing to services has resulted in increased job creation
in the service sector, reflecting the broader impact of the digital revolution on employment dynamics
(Bogliacino & Pianta, 2010). Therefore, Advanced technologies, favor skilled over unskilled labor,
causing wage disparities and increasing demand for educated workers. This has resulted in higher
unemployment among unskilled workers and greater demand for skilled labor (Vivarelli, 2014).
Skills Mismatch: In the past decades, employment growth was most rapid for jobs requiring
nonroutine cognitive tasks, which are highly complimented by computerization. Conversely, jobs
requiring routine cognitive and manual tasks, which are more easily replaced by computers, saw declining
growth. Low-wage jobs involving nonroutine manual tasks did not experience the same level of decline,
underscoring the complex dynamics of labor demand shifts (Autor et al., 2006). Nowadays, employment
growth is mostly rapid in high-skills demanding jobs regarding the rapid technological advances in most
industries. Therefore, lower-educated employees in larger firms are at higher risk of displacement due to
skill gaps. Therefore, the shift to digital processes requires workers to acquire new skills and benefit from
robust social security and career transition support systems (Bogliacino et al., 2012; Spencer, 2018),
besides the need for lifelong learning policies and modernizing education to address digital skill gaps,
minimize disruption, and ensure continued employability (Arntz et al., 2017).
Macroeconomic Adjustment: macroeconomic conditions collectively shape the overall economic
environment, influencing job security (Shoss, 2017), income levels, and the general well-being of
individuals and businesses. These conditions (e.g., Economic Growth, Unemployment Rate, Inflation
Rate, Interest Rates, Business Investment, Government Policies…etc.) may lead to economic
vulnerabilities alongside demographic characteristics and national social safety net policies. However,
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country-level expenditures on labor market initiatives (e.g., job search assistance, worker training) and
income security measures (e.g., unemployment benefits) mitigate the impact of job insecurity on job
attitudes (Shoss, 2017). Overall, macroeconomic conditions interact with technological advances to shape
labor market imbalances, influencing factors such as unemployment rates, skill mismatches, income
inequality, job polarization, labor force participation, and government policies. Effective policy responses
to labor market imbalances require considering technological and economic dynamics.
Technological advancements especially artificial intelligence significantly transform labor
dynamics by enhancing productivity through better predictions and creating new decision tasks,
consequently generating fresh job roles (Agrawal et al., 2019). Moreover, AI-driven advancements can
stimulate demand for labor in related tasks, reshaping the future workforce landscape (Agrawal et al.,
2019). Hence, technological advancements have ushered in more flexible work arrangements, such as
telecommuting, blurring the traditional work-home separation and altering how work is performed (Mokyr
et al., 2015). However, investing in education and skills training is vital to counteract the adverse effects
of automation and digital technologies on middle-class jobs (Mokyr et al., 2015). Since middle-skill jobs
now demand skills that complement technological advancements, underscoring the necessity for
investment in human capital (Acemoglu & Restrepo, 2019).
The digital revolution significantly enhances productivity growth across sectors, akin to major
technological advancements in history such as the steam engine and ICT (Graetz & Michaels, 2018).
However, employment in service occupations, particularly those involving non-routine manual tasks, has
observed significant growth over decades due to digital advancements (Acemoglu & Autor, 2011). The
expansion of the service sector emerges as a crucial driver of job creation, unlike the decline of the
industrial sector, although posing challenges in terms of productivity and wage levels (Hall, 2001), and
confronts the traditional employment strategies (Goos & Manning, 2007). Therefore, the ascent of the
service sector necessitates more adaptable labor market regulations to withstand economic shocks and
maintain competitiveness (Hall, 2001). However, this transition may exacerbate income inequality, as the
demand for low-wage service jobs escalates, challenging the maintenance of social benefits and wage
structures (Hall, 2001).
4.2.3 Technological Unemployment in Terms of Global Labor Market Imbalances:
Historical economic theories, including those from Marx, Keynes, and Schumpeter, highlight how
technological innovation has been a source of economic growth and unemployment. The current wave of
automation continues this trend, with significant implications for global labor markets (Spencer, 2018).
Technological unemployment occurs when process innovations boost productivity, reducing labor
demand. The impact varies by country and is influenced by production efficiency and labor market
conditions (Harrison et al., 2014). In Germany and the UK, these innovations significantly cut labor
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demand, while in France and Spain, the effect is less severe (Harrison et al., 2014). Technological
unemployment, spurred by robotics and AI, creates global labor market imbalances, particularly affecting
middle- and low-skilled jobs, which face greater risks of automation. This increases inequalities in
employment, wages, and work hours across different skill levels and regions (Arntz et al., 2017).
Technological advancements have led to job losses in routine task sectors, contributing to
technological unemployment. The growth in non-routine cognitive tasks has created new jobs for educated
workers, exacerbating global labor market imbalances (Autor et al., 2003; Autor & Dorn, 2013; Vivarelli,
2014). As industries adopt computer technology, routine task jobs decline and low-skill service jobs rise,
leading to employment and wage polarization, reflecting unbalanced technological progress (Autor &
Dorn, 2013). In other words, the balance between automation and task creation determines the overall
labor market impact. In the long term, labor may still coexist with technological advancements but
inequality might increase during transitions (Acemoglu & Restrepo, 2018). For instance, low-qualified
workers are vulnerable to job automation and digitization, which requires re-training (Arntz et al., 2017).
Technological unemployment arises due to global labor market imbalances caused by routine-
biased technological change (RBTC) and offshoring by moving routine jobs from high-income countries
to lower-cost countries, leading to uneven employment opportunities across regions and skill levels (Goos
et al., 2014), with developing countries potentially facing higher job losses due to automation (Spencer,
2018). Lower-skilled jobs are more vulnerable to automation, while jobs requiring problem-solving and
social intelligence are less affected (Bogliacino et al., 2012). Although new tasks can offset some negative
employment impacts, the shift towards capital-intensive production and market preference for excessive
automation necessitate policy interventions (e.g., adaptive education and training systems). Such policies
are crucial to avoid job polarization and decreased routine employment (Goos et al., 2014; Acemoglu &
Restrepo, 2018), to balance automation with job creation, and to address global labor imbalances
(Acemoglu & Restrepo, 2018).
Automation's risk across the global labor market differs, with certain job types and skill sets being
more vulnerable. Men, lower-skilled workers, and employees in larger private-sector firms are particularly
prone to automation (Pouliakas, 2018). To address the resulting imbalances, adaptable education and
vocational training systems are crucial in facilitating workers' transition to new roles (Arntz et al., 2017).
In addition, policymakers must prioritize the development of robust social security measures alongside
adaptable education systems to mitigate the adverse effects of technological unemployment (Pouliakas,
2018). Also, understanding the delicate balance between automation and human labor is essential, guiding
policymakers in crafting effective policies that can alleviate negative impacts on the labor market globally
(Autor, 2015).
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5 DISCUSSION
Technological advancements, while initially disruptive, are anticipated to boost productivity,
wages, and overall living standards (Mokyr et al., 2015). Although automation may result in short-term
job displacement, it simultaneously paves the way for new employment opportunities over time (Mokyr
et al., 2015). Moreover, economic principles such as comparative advantage enable workers to maintain
valuable roles; however, the extent of Industry 4.0’s impact varies across regions and industries,
contingent on regulatory frameworks and technological integration (Acemoglu & Restrepo, 2019;
Agrawal et al., 2019). Public policies, therefore, play a fundamental role in facilitating these transitions,
while institutional differences among market economies further shape responses to unemployment (Hall,
2001). Additionally, key industries—including Electronics, Energy Technology, and Consumer Goods—
are at the forefront of robot adoption, with industrial robot density increasing significantly (International
Federation of Robotics, 2023). Thence, both job displacement and creation are expected in sectors such
as Oil and Gas, as companies adapt to macro trends, including digital transformation, demographic shifts,
and green transitions (World Economic Forum, 2023).
The global economy is undergoing substantial workforce restructuring, with up to 47% of jobs in
the United States deemed vulnerable to automation, whereas Western Europe is projected to generate
additional employment opportunities (Liboni et al., 2019). Therefore, the evolving labor landscape
necessitates that Human Resource Management (HRM) strategies align with Industry 4.0, prioritizing
digital literacy, problem-solving, and adaptability (Liboni et al., 2019). Furthermore, while automation
poses risks to various professions, highly skilled occupations that require creativity, critical decision-
making, and complex problem-solving remain less susceptible (Future of Jobs Report 2023). Large urban
areas with well-educated populations are, moreover, better positioned to counteract automation's adverse
effects, thereby reducing the risk of technological exclusion (Zemtsov, 2020). Ultimately, although
technological disruptions may significantly reshape labor markets, historical patterns indicate that they do
not result in prolonged unemployment increases (Zemtsov, 2020).
6 CONCLUSION AND FUTURE DIRECTIONS
The impact of technological transitions on labor markets reveals both opportunities and challenges.
I4.0 advancements, such as artificial intelligence and automation, promise increased productivity and new
job sectors but also raise concerns about job displacement, inequality, and TU. This complex landscape
underscores the need for adaptive policies and workforce strategies, emphasizing the importance of
lifelong learning and adaptable education systems. While technological change may disrupt traditional
employment, it also offers potential for economic growth and innovation. Effective collaboration among
policymakers, businesses, and individuals, along with strategic investments in education and social
security, is crucial to ensure inclusive growth and mitigate adverse impacts, ultimately shaping a future
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where technological progress benefits all.
Like other research efforts, this study has limitations that could be improved upon in future
research. Utilizing additional research databases such as Scopus, Web of Science, Microsoft Academic,
and SpringerLink, among others, might provide a more varied dataset related to I4.0, TU, and LMI,
thereby improving the quality of the findings. Furthermore, combining databases (e.g., WoS and Scopus)
could enhance the sample size and the overall quality of the research outputs, making the results more
applicable. In addition, it is possible to investigate the impact of I4.0 on TU among sectors/countries,
besides their implications on the Labor market globally/nationally.
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