Artificial Intelligence and Human Resources Management: A Bibliometric Analysis

Abstract

Artificial Intelligence (AI) is increasingly present in organizations. In the specific case of Human Resource Management (HRM), AI has become increasingly relevant in recent years. This article aims to perform a bibliometric analysis of the scientific literature that addresses in a connected way the application and impact of AI in the field of HRM. The scientific databases consulted were Web of Science and Scopus, yielding an initial number of 156 articles, of which 73 were selected for subsequent analysis. The information was processed using the Bibliometrix tool, which provided information on annual production, analysis of journals, authors, documents, keywords, etc. The results obtained show that AI applied to HRM is a developing field of study with constant growth and a positive future vision, although it should also be noted that it has a very specific character as a result of the fact that most of the research is focused on the application of AI in recruitment and selection actions, leaving aside other sub-areas with a great potential for application.

Full text

Articial Intelligence and Human Resources Management:
A Bibliometric Analysis
P.R. Palos-Sánchez
a
, P. Baena-Luna
b
, A. Badicu
c
, and J.C. Infante-Moro
d
a
Financial Economics and Operations Management, University of Seville, Seville, Spain;
b
Business
Administration and Marketing, University of Seville, Seville, Spain;
c
Economics and Business, Open
University of Catalonia, Barcelona, Spain;
d
Economics and Operations Management, University of
Huelva, Huelva, Spain
ABSTRACT
Articial Intelligence (AI) is increasingly present in organizations.
In the specic case of Human Resource Management (HRM), AI
has become increasingly relevant in recent years. This article
aims to perform a bibliometric analysis of the scientic literature
that addresses in a connected way the application and impact of
AI in the eld of HRM. The scientic databases consulted were
Web of Science and Scopus, yielding an initial number of 156
articles, of which 73 were selected for subsequent analysis. The
information was processed using the Bibliometrix tool, which
provided information on annual production, analysis of journals,
authors, documents, keywords, etc. The results obtained show
that AI applied to HRM is a developing eld of study with
constant growth and a positive future vision, although it should
also be noted that it has a very specic character as a result of
the fact that most of the research is focused on the application
of AI in recruitment and selection actions, leaving aside other
sub-areas with a great potential for application.
ARTICLE HISTORY
Received 24 June 2022
Revised 1 November 2022
Accepted 4 November 2022
KEYWORDS
Artificial intelligence; human
resources management;
Bibliometrix; personnel
recruitment; emerging
technologies
Articial Intelligence: A New Paradigm in Human Resource Management
The supposed “Fourth Industrial Revolution” or “Industry 4.0” has introduced
intelligent technologies like Artificial Intelligence (AI) (Kong et al. 2021). The
increased development of information and communication technologies
(ICT) allows phenomena like AI to greatly influence different parts of society
(Bolander 2019) becoming one of the most relevant elements of all possible
changes in various aspects of life in this era (Aloqaily and Rawash 2022)
Although different departments of multiple organizations have adopted or
integrated AI-based tools, the Human Resources (HR) department still cannot
implement them (Vrontis et al. 2022). Despite there being many people in the
HR department of organizations that recognize the importance of applying AI,
they also point out that they have not taken any actions regarding this. This is
a reality that shows that even though AI in the HR area is still a developing
CONTACT P. Baena-Luna pbaenaluna@us.es University of Seville. Ramon y Cajal Av, 1. 41018, Seville Spain
APPLIED ARTIFICIAL INTELLIGENCE
2022, VOL. 36, NO. 01, e2145631 (3655 pages)
https://doi.org/10.1080/08839514.2022.2145631
© 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/
licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.

revolution and is mostly limited to large companies (Bolton 2018), it is already
unstoppable.
Due to the relative novelty of this technology and its application in different
areas of the organization, many of the scientific developments in this field have
mostly occurred in recent years. For this reason, although AI has been
presented as a powerful tool in HRM, academic research on the subject is
not very extensive (Pan et al. 2022).
In this context, we consider that based on a bibliometric approach, the
article aims to identify and analyze the connection of the AI phenomenon with
the human resource management (HRM) of organizations to study (1) the
level of knowledge and training of their managers, (2) the benefits and
challenges in its implementation, and (3) identify the subareas with greater
development and implementation in HRM.
The connection between AI and HRM allows us to establish the following
research questions for this work. The first research question is related to
previous AI reflections and challenges. However, authors seem unclear how
AI will affect or benefit employees and societies (Mitchell and Brynjolfsson
2017). Other authors point out to the need for more data about on the speed of
AI progress (Nedelkoska and Quintini 2018). Especially its impact on every
HRM-related task.
RQ1. Does the scientific community consider AI to be a commonly used tool
in HRM?
The second proposed question has been studied by several published works
that indicate the benefits of AI technology in different HRM sub-tasks (Qamar
et al. 2021).
RQ2. Does AI have a similar impact on all HRM sub-areas?
RQ3. Are employees in HR areas prepared to meet the challenges posed by
AI in people management?
RQ4. Does the application of AI in HRM help to improve the company’s
competitiveness?
The answer to this research questions derived from the results
obtained together with the discussion and the most relevant conclusions
support the theorization presented in this paper. Regarding the origin-
ality of this work, this study, based on quantitative and qualitative
research, from the combined use of the most relevant scientific data-
bases, Web of Science and Scopus, allows us to focus on how IA has
been integrated into organizations in HRM and its influence on the
APPLIED ARTIFICIAL INTELLIGENCE e2145631-3629

approach of organizations and Human Resources. The results obtained
will allow make the following contributions. First it will serve the
research community in the AI field and its applications in the manage-
ment of people and talent in organizations as a starting point for future
related research work. Also important will be the implications for the
people responsible by allowing the knowledge of the main uses and
applications of new resources and tools in the HRM of organizations
will also be relevant and current trends in their application.
Concept of Articial Intelligence
The concept of AI has multiple definitions. Different researchers have
proposed their definitions (Welsh 2019). Depending on the time and the
level of technological development reached, different studies have
focused on its various aspects. A sample of the most relevant definitions
since the 20th century is shown in Table 1.
Despite the ambiguous origin of the concept of AI, two authors stand out in its
development. On the one hand, we have A.M. Turing, the father of modern
computation, while on the other hand, there is J. McCarthy, the father of AI.
Turing (1937) introduced the concept of algorithms and laid the foundation of
computer science. Later, Turing (1950) proposed the Turing test, which tests
whether a machine has the capacity to be as intelligent as the person performing its
functions. However, J. McCarthy coined the term “artificial intelligence” during
a conference in Dartmouth (Paesano 2021). In the 1950s and 1960s, AI was
expected to develop rapidly into computers and robots with human-level cogni-
tive capabilities, but that did not happen until it recently gained prominence
(Bolander 2019; Pillai and Sivathanu 2020).
Table 1. Definitions of Artificial Intelligence.
Authors Definitions
(McCarthy 1956) The science and engineering of creating intelligent machines, especially intelligent
computer programs.
(Minsky 1968) The science that deals with the development of machines capable of performing functions
that a human can perform and that require human intelligence.
(Nilsson 1998) AI is a part of computer science that focuses on machine learning, making computers act
intelligently, continuously learning, and improving their performance.
(Cappelli et al. 2019) Broad class of technologies that enable a computer to perform tasks that normally require
human cognition, including decision-making.
(Stanley and Aggarwal
2019)
Development of computer systems that perform tasks that require human intelligence.
The main goal of AI is to make machines more intelligent.
(Bolander 2019) Construction of machines – computers or robots – that can perform tasks that otherwise
only humans have been able to do.
(Paesano 2021) Systems that exhibit intelligent behavior by analyzing their environment and performing
actions, with a certain degree of autonomy, to achieve specific objectives.
e2145631-3630 P. R. PALOS-SÁNCHEZ ET AL.

Articial Intelligence Applied to People Management
Human capital is a differentiating element of an organization as it is an
intangible resource that is difficult for competitors to imitate, thus giving
a potential competitive advantage to any organization (Kearney and
Meynhardt 2016).
HRM has become a strategic trend in organizations due to economic,
political, social, and especially technological changes (Jatobá et al. 2019). Not
all departments have embraced this new role, and strategic positioning
remains slow and sometimes problematic (Poba-
Nzaou et al. 2020). In these cases, incorporating technologies like AI requires
the need to evolve with the other facets of society (Michailidis 2018).
The role of AI in an organization is to improve efficiency and effective-
ness of the HR function by making the various management processes agile
and accurate (Nankervis et al. 2021). For HRM, IA will enable the under-
standing and control of a data collection process so that this process is
included in an organizational and economic efficiency strategy (Varma et al.
2022). Among the different areas that make up the HRM in an organization
where AI is starting are: (1) talent search and recruitment, (2) training and
development, (3) performance analysis, (4) career development, (5) com-
pensation, and (6) staff turnover (Abdeldayem and Aldulaimi 2020; Nawaz
2020; Qamar et al. 2021; Yahia, et al. 2021)
Qamar et al. (2021) showed that AI has been implemented in HRM in
various organizations via the following techniques:
Expert Systems: They are programs designed to configure expert knowledge
into logical structures that solve unstructured problems and help develop
complete information systems by providing easy access to knowledge. It is
applied mainly in HR planning, compensation, recruitment, and labor man-
agement (Malik et al. 2022).
Fuzzy Logic: This technique is used in different research fields (Salmerón
and Palos-Sánchez 2019). In the case of HRM, it’s based on set membership
levels, whose values vary between 0 and 1. A value of 0 indicates no member-
ship, while a value of 1 shows full membership. With these sets, fuzzy logic can
quantify data uncertainty and foresee future scenarios to facilitate decision-
making (Kimseng et al. 2020). Its application began in 2000 and was used in
personnel selection and optimal workforce design (Qamar et al. 2021).
Artificial Neural Networks: This application is a simplified model developed
to mimic the function of a human brain. Its structure comprises a processing
element, a layer, and a network to recreate the human learning process (Huang
et al. 2006). It is one of the most popular techniques for prediction and is
mainly used in selection, recruitment, and personnel performance manage-
ment (Qamar et al. 2021).
APPLIED ARTIFICIAL INTELLIGENCE e2145631-3631

Data Mining: It is the extraction of valuable but hidden information.
Through its application, organizations can transform useful information and
patterns into competitive advantages (Huang et al. 2006). Data mining was
used in HRM in 2006 and has been applied mainly for recruitment, compe-
tency and performance evaluation, and talent management.
Genetic algorithm: These information search techniques based on replica-
tion, mutation, and gene crossover arrive at optimal solutions to mathematical
problems. It is used mainly in workforce planning and personnel performance
evaluation (Zhang et al. 2021).
Machine learning: It is the learning process by which a machine can learn by
itself without being particularly programmed to do so (Rąb-Kettler and
Lehnervp 2019). Several papers agree that the use of machine learning in
decision-making is quite beneficial for HR managers and turnover prediction
(Hamilton and Davison 2022).
Benets and Challenges of Articial Intelligence in Human Resources
Management
As with any technological advance, AI brings both benefits and challenges, and
its application in HRM is no different (Vrontis et al. 2022). These can be
approached from three points of view: employees, company, and society.
We highlight the following potential benefits:
Employees: The automation of repetitive and time-consuming tasks allows HR
managers to focus on those tasks that add value and require unique skills and
abilities (Pillai and Sivathanu 2020). The reduction or minimization of errors
owing to machine learning also helps improve decision-making by providing
more and better-processed information (Michailidis 2018). According to
a 2019 survey, 61% of companies were using AI to improve HRM in key AI-
transformed HRM areas. This task will include time-consuming and labor-
intensive processes in recruitment, such as reading many CVs, sorting through
them and identifying the best candidates and detect employees who need some
training (Rykun 2019)
Company: For companies, AI means greater effectiveness and efficiency as it
streamlines management processes and reduces associated costs (Nankervis
et al. 2021). It enables greater candidate outreach as it reaches passive
candidates who are not in active job search but might become interested in
the position (Black and van Esch 2021). Another important element for
companies is the improvement of communication and interaction possibi-
lities among employees (Michailidis 2018). Research articles looks at how AI
help to improve the successive stages of the recruitment process: identifying,
selecting and retaining talented people (Allal-Chérif et al. 2021).
e2145631-3632 P. R. PALOS-SÁNCHEZ ET AL.

Society: The creation of new professional profiles linked to AI, like robotics
specialists, data scientists, deep learning experts, generate new scenarios which
can benefit the public (Michailidis 2018).
As far as challenges are concerned, the following can be highlighted:
Employees: The application of AI may contribute to burnout, with some
employees being worried about their career uncertainty, since machines may
replace them, thereby creating anxiety and job insecurity (Kong et al. 2021).
There is also dehumanization of personal relationships, as some of the HRM
processes may be performed entirely by machines, like the use of chatbots
(Fritts and Cabrera 2021). This implies the continuous need for training in
technological matters. Finally, it is necessary to point out that the “techno-
stress” is a consequence of excessive and continuous use of any type of
technology (Malik et al. 2021).
Company: The need for highly qualified personnel to manage and acquire the
necessary skills to keep up with the increasing technological developments
(Abdeldayem and Aldulaimi 2020) is a reality in AI. Even though it has high
implementation costs, it can reduce costs in the processes where they are
applied (Michailidis 2018). Another challenge is the existence of biases due
to the use of small and non-representative data volumes (Soleimani et al. 2022)
and the increased exposure of the company leading to increased risk of its data
security breach (Malik et al. 2021).
Society: One of the main challenges in this area is the “technology gap” Since
technology in general and AI has divided the world, it has created greater
technological inequality. This is because not all countries can implement and
maintain technological infrastructure (Abdeldayem and Aldulaimi 2020).
Potential job losses in certain professions are also important in the face of
these challenges (Hamilton and Davison 2022).
Methodology
The methodology used was bibliometric analysis using the Bibliometrix
application. This tool was developed by Aria and Cuccurullo (2017) to
carry out comprehensive analyses of the scientific mapping of a topic. It is
an open-source tool to perform a comprehensive analysis of the scientific
literature. It was programmed in R language to be flexible and facilitate
integration with other statistical and graphical packages. Bibliometrix
enables the structured analysis of large amounts of information to infer:
(a) trends over time, (b) which topics are being investigated, (c) changes in
the boundaries of disciplines, etc., thus summarizing a topic (Guleria and
Kaur 2021).
APPLIED ARTIFICIAL INTELLIGENCE e2145631-3633

The first step was to determine the databases to be used for the document
search. The databases being queried were Web of Science and Scopus, as they
are currently the most relevant within our research field (Parris and Peachey
2013). The search keywords on both bases were “Artificial Intelligence” and
“Human Resources” in the search field (Article Title, Abstract, and Keywords)
(Macke and Genari 2019) for the period 2018–2022. This period was chosen
based in previous authors. For Kshetri (2021) AI-based HRM applications can
bring about significant changes in human resource management practices.
However, previous researchers have observed a substantial gap between the
promise and reality of AI in HRM (Michailidis 2018; Tambe et al. 2019). The
research domain of AI in HRM is relatively nascent (Strohmeier and Piazza
2013). Garg et al. (2022) note the narrowing of the gap between the number of
journal and conference papers from 2017 onwards: a decrease in conference
papers with a simultaneous increase in journal papers shows the increasing
confidence, interest, and acceptance for AI, especially Machine Learning (ML).
Based on all this, the choice was justified because (1) it had the highest
number of publications on the issue, (2) it had an interest in the topic, and (3)
the previous literature does not correspond to the current technological level.
Subsequently, the scientific fields selected for the query were (1) Business,
(2) Management and Accounting, (3) Arts and Humanities, (4) Social
Sciences, (5) Economics and Finance, and (6) Psychology and Research
Management. These areas were chosen since they were directly related to
our current scenario. The scientific fields that could contribute the least to
research, such as physics, biology, medicine, etc., were eliminated. The ana-
lyzed works were those written in English to cover a larger number of pub-
lications (Gutiérrez and Maz 2004) and limited to those publications that were
only articles (Podsakoff et al. 2005) excluding works corresponding to the
following types of documents: (a) book, (b) book chapter, (c) proceedings
paper, (d) review and (e) editorial material (Vlačić et al. 2021).
Once the search string was established and the corresponding filters
applied, we obtained 156 articles. As shown in Figure 1, among the 89 articles
initially obtained (63 in the Scopus database and 26 in Web of Science), 9 were
rejected after further analysis of their content. This resulted in 80 valid articles
for the study. Finally, 7 of these were eliminated as duplicates were found.
Thus, we finally obtained 73 papers due to the harmonization of the results.
The final articles were exported from the databases in their respective
formats; Plain text and BibTeX for Web of Science and Scopus, respectively.
They were then integrated into a single format to be imported later into the
Biblioshiny platform and further data analysis was carried out. Before proces-
sing the data with this software, the following steps were adopted: (1)
Download and install the latest version of R and RStudio (https://cran.r-pro
ject.org/and https://www.rstudio.com) (2) Open RStudio and in the console
window type the following command to finish the installation of Bibliometrix;
e2145631-3634 P. R. PALOS-SÁNCHEZ ET AL.

install. packages (“bibliometrix”) (3) Type the following command to be able to
run the Biblioshiny program: library (bibliometrix) biblioshiny ()
According Iden and Eikebrokk (2013) and to the established inclusion and
exclusion criteria the data extracted from each study were as follows: (1) the
journal and full reference, (2) the authors and their institutions, (3) the
countries where they were situated, (4) the keywords, (5) classification of the
research methods, (6) theoretical frameworks and references theories used, (7)
main topic area, (8) research questions and (9) a summary of the study.
The critical examination of the content of each article (Bellucci et al. 2021)
together with the use of the Bibliometrix tool, in particular, by means of
Multiple Correspondence Analysis (MCA), made it possible to establish
three thematic clusters: (1) AI in HRM, (2) Digital Recruitment and (3)
Electronic HR. According Paul et al. (2021) the systematic review of a topic
in depth and with rigor favors both the theory on an area and the research
methodology in that field can benefit, this is our purpose with the development
of this work in the field of IA and HRM.
Results
AI is undoubtedly one of the most important innovations. Both academics and
practitioners hope that IA can solve this problem and offer a solution to
support and streamline innovation processes. However, the literature on this
topic is fragmented (Pietronudo et al. 2022). These authors concluded that AI
renews the organization of innovation and AI triggers new challenges. That is,
they suggest that AI is not a tool that uniformly optimizes innovation manage-
ment and decision-making but is better understood as a multifaceted solution.
Similar conclusions can be reached by first analyzing other systematic
literature reviews (SLRs) and a bibliometric analysis. Table 2 shows SLR
Figure 1. Identification of articles to be analyzed.
APPLIED ARTIFICIAL INTELLIGENCE e2145631-3635

works approach these reviews from different points of view applied to a greater
or lesser extent to different HRM processes.
The 73 articles were published in 53 different journals and represented 199
different authors (Table 3). The average number of annual publications repre-
sents an average of < 1 article per year, thereby indicating, at first glance, that
the field of AI being applied to HRM is underdeveloped. However, as shown in
Figure 2, it is a topic of great interest in the immediate future.
The annual distribution of the number of articles shows the general state of
research and trends, with exponential growth occurring only in the last five
years. Advances and growth in the importance of AI in both academia and HR
(Jatobá et al. 2019) have sparked increased interest in investigating the influ-
ence of one topic on the other. Although there were only two articles in 2017
addressing the concepts in a connected way, the number increased to 10 in
2019. The trend line shows that AI will soon persist in the future as one of the
top world innovations (Qamar et al. 2021) with an annual growth rate: 64.38%.
Analysis of Sources
Table 4 shows, in order from the highest to the lowest number of articles, the
main journals that published on these analyzed realities. The journal with the
highest number was “International Journal of Manpower” with six articles,
followed by the “International Journal of Human Resource Management” and
the “Business Horizons” with five and four articles, respectively. The journals
with the highest number of publications on these topics were journals related
to business or technology, with a focus on HR, like “Advances in Developing
Human Resources and/or Human Resources Management,” is also gradually
gaining importance.
Another fundamental indicator called “Bradford’s Law” was used to analyze
the main journals and their importance in the field (Bradford 1976). This law
allows researchers to access those journals that provide the most information
on a topic, thus reducing their search times (Figure 3).
Another fundamental indicator called “Source Growth” was used to analyze
the main journals and their importance in the field. This figure allows
researchers to know the evolution of those journals (see Figure 4). The
journals “Ethics and Information Technology” and “International Journal of
Human Resource Management” present an important growth trend.
The most cited journals were “Business Horizons,” “International Journal of
Human Resource Management” and “International Journal of Manpower,”
with 138, 87, and 12 citations, respectively (see Table 5).
The g-index is calculated from the distribution of citations of an author’s
publications, which results in a set of articles ranked in decreasing order by the
number of citations they have. The Hirsch index (h-index) uses the set of the
author’s most cited articles and the number of citations it has received in other
e2145631-3636 P. R. PALOS-SÁNCHEZ ET AL.

Table 2. Previous AI and HRM Literature Overviews.
Authors Type/Period Data sources
a
Context
Screened
works/
primary
studies Methodology based
(Vrontis
et al.
2022)
SLR
unspecified
3,4 Holistic SLR on
HRM strategies,
namely: job
replacement,
human-robot/AI
collaboration,
decision-
making,
learning
opportunities,
and HRM
activities:
recruiting,
training and job
performance
45/187 (Tranfield, Denyer, and Smart
2003); (Crossan and
Apaydin 2010)
(Votto et al.
2021)
SLR
2014–2020
1,3,5,6 Explore Tactical
HRIS literature
and come to
understand
which
components are
exist in
literature and
how they are
further
represented.
33/697 Tactical HRIS
(T-HRIS) components
(Garg et al.
2022)
SLR
2002–2018
2 Semi-systematic
literature
review;
understand
current state of
Machine
Learning (ML)
integration
within HRM;
showcase
relationship
between HR
experts and ML
specialists
105/168 (Wong et al. 2013) (Snyder
2019)
(Qamar
et al.
2021)
SLR
−July 2020
2 SLR of AI and
HRM to capture
current state-of-
the-art and
prepare for new
research
agenda
59/308 (Tranfield, Denyer, and Smart
2003) (Pickering and Byrne
2014)
(Di Vaio
et al.
2020)
SLR/Bibliom
1990–2019
1,15 Comprehensive
review of
relationship
between AI and
sustainable
business
models, in
special
Sustainable
Development
Goals (SDGs).
The SLR paper
aims to
highlight
the role of
Knowledge
Management
Systems (KMS).
73/88 Identify research gaps
between knowledge
management systems and
AI
(Continued)
APPLIED ARTIFICIAL INTELLIGENCE e2145631-3637

Table 2. (Continued).
Authors Type/Period Data sources
a
Context
Screened
works/
primary
studies Methodology based
(Basu et al.
2022)
SLR un-
specified
1,5,6 AI – HRM
Interactions and
Outcomes:
A SLR and
Causal
Configurational
Explanation.
Content
analysis and
thematic
abstraction.
100/433 (Denyer and Tranfield 2009)
(Coron
2022)
SLR 2000-
April 2020
7 based on an
integrative
synthesis of
empirical and
non-empirical
articles on the
use of
quantification in
HRM.
Integrative and
systematic
synthesis
procedure
94/103 (Briner and Denyer 2012)
(Bilan et al.
2022)
Bibliom.1983–
2020
2 Bibliometric
Review of AI
Technology in
Organizational
Management,
Development,
Change and
Culture
191/218 unspecified
(Bhatt and
Muduli
2022)
SLR 1996-July
2021
8,9,10,11,12,13,14 AI in learning
and
development
81/115 (Tranfield, Denyer, and Smart
2003); (Crossan and
Apaydin 2010)
(Pereira
et al.
2021)
SLR 1995–
2020
1,3 Impact of AI on
workplace
outcomes:
A multi-process
perspective.
Limit to peer-
reviewed
journals ranked
3, 4 or 4* in the
AJG (formerly
ABS) 2018
journals.
56/211 (Tranfield, Denyer, and Smart
2003)
(Perello and
Tuffaha
2021)
SLR
2010-may
2020
2,13 AI definition,
applications
and adoption in
Human
Resource
Management
66/559 (MacKenzie et al. 2012);
(Denyer and Tranfield 2009)
a
1: ISI Web of Science; 2:Scopus; 3: Business Source Ultimate (EBSCO); 4: Science Direct; 5: AIS; 6: ABI; 7: Journal
Quality List (JQL) database; 8: Emerald; 9:Taylor & Francis; 9: Springer; 10: Sage Publications; 11: Massachusetts
Institute of Technology Sloan Management; 12: Harvard business; 13: Science
e2145631-3638 P. R. PALOS-SÁNCHEZ ET AL.

publications. The m-index is defined as H/n, where h is the h-index and n is
the number of years elapsed since the scientist’s first publication (Aria and
Cuccurullo 2017).
Analysis of Authors
Out of 199 authors, 187 published one article, nine published two articles and
three published three articles. The authors J. Black, N. Nawaz and P. van Esch
stand out (Table 6). K. Chaitanya and V. Prikshat V. start in 2021 his first
article, having published a new paper every year since their first published
article.
Regarding the impact rate of these authors, Table 7 shows that once again, the
authors J. Black, N. Nawaz and P. Van Esch have the highest index (h-index of 2),
which is double the average of the other authors, i.e., 1.
The affiliation of the authors is diverse, as shown in Table 8. Two univer-
sities stand out from the rest, i.e., University of Turin, Kingdom University
and Auckland University of Technology, with the highest number of articles of
four, three and two each, respectively.
The publication of articles from diverse countries reflects the subject’s global
importance. Seventeen countries published papers related to the application of AI
in HRM (Table 9). The countries with the highest number of publications were the
USA, India and China, with 13, 9 and 8 articles, respectively.
Although New Zealand is not the country with the highest scientific
research output, it stands out after USA (Table 10), because its articles have
Table 3. Summary of bibliographic information.
Description Results
MAIN INFORMATION ABOUT DATA
Period Time 2017:2022
Sources (Journals, Books, etc) 53
Documents 73
Annual Growth Rate % 64.38
Document Average Age 1.33
Average citations per doc 6.699
References 2511
DOCUMENT CONTENTS
Keywords Plus (ID) 220
Author’s Keywords (DE) 304
AUTHORS
Authors 199
Authors of single-authored docs 13
AUTHORS COLLABORATION
Single-authored docs 14
Co-Authors per Doc 2.93
International co-authorships % 8.219
DOCUMENT TYPES
article 66
article; early access 4
review 3
APPLIED ARTIFICIAL INTELLIGENCE e2145631-3639

the highest number of citations and therefore have the highest impact on the
related scientific literature.
Analysis of Documents
The purpose of this study was to identify the most relevant and cited articles.
Table 11 shows how Dabirian et al. (2017) is the article with the highest number of
total citations. It has been cited 89 times, with an average annual citation rate of
14.83 citations. These authors argue that as employees use information technol-
ogies to openly share and access work-related experiences across organizations,
Figure 2. Annual evolution of publications.
Table 4. Sources with the largest number of related publications.
Sources Articles
International Journal of Manpower 6
International Journal of Human Resource Management 5
Business Horizons 4
International Journal of Technology Management 3
Journal of Management Information and Decision Sciences 3
Advances in Developing Human Resources 2
Computers in Human Behavior 2
Ethics and Information Technology 2
International Journal of Scientific and Technology Research 2
Asia Pacific Journal of Human Resources 1
Benchmarking 1
BPA Applied Psychology Bulletin 1
California Management Review 1
Cyprus Review 1
Employee Responsibilities and Rights Journal 1
European Journal of Information Systems 1
Forum Scientiae Oeconomia 1
Frontiers in Psychology 1
Human Resource Management 1
Human Resource Management Review 1
e2145631-3640 P. R. PALOS-SÁNCHEZ ET AL.

their expectations and evaluations of workplaces change. Using a data collection of
38,000 reviews of the best and worst rated employers on Glassdoor,
a crowdsourced online employer branding platform, they concluded that employ-
ers could use AI to become great workplaces that attract highly skilled employees.
Spectroscopic analysis: According to Marx et al. (2014), Reference
Publication Year Spectroscopy (RPYS) is a quantitative method for identifying
the historical origins of a research field. It creates a temporal profile of cited
references for a set of papers, thus highlighting the period in which relatively
Figure 3. Bradford Law.
Figure 4. Source Growth.
APPLIED ARTIFICIAL INTELLIGENCE e2145631-3641

Table 5. Impact of sources.
Sources h_indexx g_indexx m_index TC NP PY_start
Business Horizons 3 4 0.5 138 4 2017
International Journal of Human Resource Management 3 5 3 87 5 2022
International Journal of Manpower 2 3 1 12 6 2021
International Journal of Scientific and Technology Research 2 2 0.667 14 2 2020
Journal of Management Information and Decision Sciences 2 2 0.667 4 3 2020
Advances in Developing Human Resources 1 1 0.333 2 2 2020
Asia Pacific Journal of Human Resources 1 1 0.5 4 1 2021
Benchmarking 1 1 0.333 12 1 2020
California Management Review 1 1 0.25 61 1 2019
Computers in Human Behavior 1 2 0.25 25 2 2019
TC: Total citations. PY_start: Year of publication start
Table 6. Relevant authors.
Authors (>=2 articles) Articles Articles Fractionalized
Black J. 3 1.50
Nawaz N. 3 2.33
van Esch. P. 3 1.50
Avrahami D. 2 0.50
Chaitanya K. 2 0.50
Chiappetta J. C. 2 0.45
Mcneese N. 2 0.45
Pessach D. 2 0.50
Prikshat V. 2 0.45
Schelble B. 2 0.45
Singer G. 2 0.50
Wang X. 2 0.67
Table 7. Author impact factor.
Authors h_index g_index m_index TC NP PY_start
Black J. 2 3 0.5 49 3 2019
Nawaz N. 2 2 0.667 4 3 2020
van Esch. P. 2 3 0.5 49 3 2019
Сhulanova O. 1 1 0.25 5 1 2019
Abdeldayem M. 1 1 0.333 11 1 2020
Aggarwal V. 1 1 0.25 2 1 2019
Agrawal R. 1 1 0.5 1 1 2021
Aich A. 1 1 0.333 3 1 2020
Akar C. 1 1 1 2 1 2022
Akshay P. 1 1 0.333 1 1 2020
Table 8. Authors affiliations.
Affiliation Articles
University of Turin 4
Kingdom University 3
Auckland Univ. Technol. 2
Clemson University 2
Coventry University 2
Lomonosov Moscow State University 2
Neoma Business School 2
Shandong University 2
University of Nicosia 2
University of Reading 2
e2145631-3642 P. R. PALOS-SÁNCHEZ ET AL.

significant findings were published along with the temporal roots of
a discipline.
Figure 5 shows how AI as a technology and its use in HRM has evolved and
its interest in publishing-related work has increased. Spectroscopic analysis
began in 1980, when related publications started appearing, although as
depicted in Figure 5, it was not until 2017 that there was a notable increase
in the related scientific production.
The first upturn occurred in 2000, and disruptive technology gained wide-
spread importance during the early 2000s. Ever since then, changes have been
observed in how organizations operate and how HRs are managed (Minbaeva
2021). Until the early 1980s, 70–90% of the company’s value was linked to
tangible assets. However, since 2000, the value linked to intangible assets has
increased to 65%, with people being the “cogs in the wheel of intangible assets”
(Black and van Esch 2021). Two AI techniques are being used in HRM: fuzzy
logic and artificial neural networks, both of which aid in the optimal workforce
design and performance management.
The second upturn occurred in 2006 when knowledge management became
a field of greater importance even though it was already being studied. Since
intangible factors had already become more important, there was a greater
need for HRM to obtain competitive advantages. Using data mining will be the
Table 9. Scientific produc-
tion by countries.
Country Freq
USA 13
India 9
China 8
UK 4
New Zealand 3
Brazil 2
Denmark 2
France 2
Italy 2
Australia 1
Table 10. Average number of citations of articles by
country.
Country TC Average Article Citations
USA 67 9.57
New Zealand 49 16.33
China 38 5.43
Italy 29 9.67
India 15 3.75
Denmark 7 3.50
Cyprus 6 6.00
UK 5 1.67
France 1 1.00
Poland 1 0.50
APPLIED ARTIFICIAL INTELLIGENCE e2145631-3643

key to correctly assessing competencies and performance. Through these
evaluations, it will promote the exchange of knowledge among employees,
along with the generation of new ideas and business opportunities.
Finally, the greatest upturn occurred in 2018, since it is from this year that
the study on AI being applied to HRM began gaining importance. The endless
possibilities of AI automation generate interest in its application in HRM
(Jatobá et al. 2019).
Table 11. Most cited articles.
Article (Authors/Journal) Total Citations TC Per Year
(Dabirian et al. 2017)/Business Horizons 89 14.83
(Tambe et al. 2019)/California Management Review 61 15.25
(Vrontis et al. 2022)/The International Journal of Human R. M. 51 51.00
(Caputo et al. 2019)/Management Decision 29 7.25
(Black and van Esch 2020)/Business Horizons 26 8.67
(Suen et al. 2019/ Computers in Human Behavior 24 6.00
(van Esch and Black 2019)/Business Horizons 21 5.25
(Gupta et al. 2018)/Journal of Information Technology Teach Classes 17 3.40
(Malik et al. 2022)/The International Journal of Human R. M. 16 16.00
(Giermindl et al. 2022)/European Journal of Information Systems 14 14.00
(Pan et al. 2022)/The International Journal of Human Resource M. 14 14
(Pillai and Sivathanu 2020)/Benchmarking 12 4
(Abdeldayem and Aldulaimi 2020)/Internatio. J. of Scientific & T. R. 11 3.67
(Kong et al. 2021)/International Journal of Contemporary H. M. 9 4.5
(Arslan et al. 2022)/International Journal of Manpower 9 9
(Ogbeibu et al. 2022)/Journal of Intellectual Capital 9 9
(Michailidis 2018)/Cyprus Review 6 1.2
(Vinichenko et al. 2019)/International Journal of Recent T. and E. 5 1.25
(Sahota 2019)/IEEE Engineering Management Review 5 1.25
(Boustani 2022)/Journal of Asia Business Studies 5 5
Figure 5. Annual Spectroscopic Analysis of publications.
e2145631-3644 P. R. PALOS-SÁNCHEZ ET AL.

Keyword analysis: Keywords are essential for a bibliographic search. Their
identification and analysis are crucial for gaining in-depth knowledge of the
articles’ content and the topics being analyzed.
The most impactful frequent keywords related to AI application in HRM
are AI, HR, Management, and Machine Learning. The importance of the AI
concept stands out, but to a lesser extent than that of HRM. AI is experiencing
an increase in its application in various fields, but as far as HRM is concerned,
it has not yet occurred completely.
Knowledge Structures Analysis
Conceptual structure: It refers to what the science is about, the main themes,
and trends. Specifically, multiple correspondence analysis (MCA) helps ana-
lyze categorical data to reduce large sets of variables into smaller sets to
synthesize the information in the data (Mori et al. 2014). To do this, the
data are compressed into a low-dimensional space to form a dimensional or
three-dimensional graph that uses planar distance to reflect the similarity
between keywords.
Three clusters or groups of content are highlighted:
(1) Cluster 1 (AI in HRM): In this first cluster, the AI tools being applied in
HRM are addressed to highlight big data and machine learning. With
big data, this might support decision-making processes, since large
amounts of varied data from various sources can be quickly analyzed,
resulting in a stream of actionable knowledge (Caputo et al. 2019). As
for machine learning, the last decade has accelerated its use and applic-
ability owing to the availability and variety of data (Hamilton and
Davison 2022). This type of learning provides systems with the ability
to learn (Soleimani et al. 2022) and mimic human skills (Bolander
2019). Machine learning can learn from the current context and gen-
eralize what it has learned to a new context. There are many organiza-
tions that, despite not comprehensively using AI in HRM, use this type
of algorithm (Nankervis et al. 2021).
(2) Cluster 2 (Digital Recruitment): It is the use of ICTs to attract potential
candidates, keep them interested in the organization during the selec-
tion processes, and influence their employment choice decisions
(Johnson et al. 2021). Pillai and Sivathanu (2020) point out how talent
acquisition has become a crucial function for HR managers, with
organizations going to great lengths to attract the best talent.
For van Esch and Black (2019), talent acquisition has changed from
a tactical HR activity to a business priority. The basis of competitive
APPLIED ARTIFICIAL INTELLIGENCE e2145631-3645

advantage has shifted from tangible assets to intangible assets, thereby
increasing the strategic importance of human capital to make it the key
driver. The shortage of talent in the labor market has intensified the need for
human capital.
The traditional method of searching for candidates used to be a slow and
costly process. However, today, due to technological advances and digital
recruitment, it is much easier and cheaper. Furthermore, since nowadays
most of society is spending increasing time in the digital space, if companies
want to attract and recruit talent, they have to do it in that space (Black and
van Esch 2021).
3) Cluster 3 (Electronic HR): This cluster presents a much more “futuristic”
vision of HR which involves complete digitalization and the use of robots in
daily functions.
While electronic HR management stands out in using technology to facil-
itate HRM processes like, recruitment, selection, training, performance man-
agement, human resource planning, compensation, etc. (Johnson et al. 2021).
Through ICTs, it is possible to achieve better control of performance and over
the employees’ behavior for greater strategic and effective management.
Using robots in HRM also stands out. Future forecasts are that in 20 years,
robots will be in charge of making some analytical decisions that are now being
made by human managers, while humans will continue to be in charge of tasks
like creativity (Stanley and Aggarwal 2019).
Social structure: It shows how authors or countries are related in a research
field; the most commonly used is the co-authorship network (Aria and
Cuccurullo 2017). The authors who stand out for having the highest number
of shared publications are Black & van Esch and McNeese & Schelble. In
general terms, there is a high degree of cooperation between authors in the
publication of articles, and very few publications are being written by a single
author.
In terms of collaboration between countries, the USA is the country with
the highest number of collaborations. Whereas with New Zealand and with
France, it should be noted that collaboration with the first country is much
greater than with the second. Also, there are other collaborations between
Brazil and Portugal, China and the United Kingdom, Germany, and Norway-
Tunisia.
Discussion
The research questions initially raised after the results were obtained and the
studies analyzed can be answered as follows.
Q1: AI is not yet commonly used in HRM. However, its use has acquired
greater relevance in the last five years, with 2021 being the year with the
highest number of publications. Authors like Cappelli et al. (2019) assert
e2145631-3646 P. R. PALOS-SÁNCHEZ ET AL.

that the application of AI in HRM has not advanced as expected. Among the
main barriers are: the complexity of HR phenomena, associated data chal-
lenges, equity and legal constraints, and employee reactions. Poba-
Nzaou et al. (2020) states that even though the “Fourth Industrial
Revolution” again highlighted the need for people to be at the center of
organizations, it seems that HR departments remain unprepared to take
advantage of this new opportunity. Nankervis et al. (2021) point out that as
technology advances, it will be impossible for the traditional HRM approach to
not advance as well; in fact, the forecast is that over the next decade there will
undergo a significant change. However, any research article indicates that
social entrepreneurship will use the opportunities of Industry 4.0 to optimize
its processes until 2030, but will decline complete automation, using human
intellect and AI at the same time (Popkova and Sergi 2020)
Q2: The results obtained show that the literature has largely focused on
the analysis of the application of AI in personnel selection. Qamar et al.
(2021) pointed out that, although AI is becoming increasingly important for
HR, instead of trying to take advantage of this tool to apply it to the entire
people management process, they focused only on a specific sub-area. It is
meaningless to attract the best talent if you don’t have the tools to manage
it. As highlighted by Nankervis et al. (2021), the automation of certain
complex processes will require increasingly highly trained and qualified
personnel.
Q3. The reviewed literature highlights that most employees still do not
welcome the application of AI in HRM. Nankervis et al. (2021) show that
many HR professionals lack the necessary skills and competencies to meet the
challenges of AI application in HR processes, hence their possible contrary
attitude. Fritts and Cabrera (2021) highlighted the concern of HR professionals
against the use of recruitment algorithms, as they can dehumanize the hiring
process. Vinichenko et al. (2019) highlighted how many employees lacked
confidence in the integrated use of machines in the management processes
because they feared being replaced by machines. However, this is unlikely, since
even if some tasks are fully automated, the human factor will not disappear
completely (Johnson et al. 2021; Kong et al. 2021).
Q4. A company gaining a competitive advantage involves several factors
like customer satisfaction through quality service, cost optimization, innova-
tion, productivity, etc. The primary function of any technology, specifically AI,
is to improve the efficiency and effectiveness of the HR function to help make
recruitment, retention, and management easier and more accurate, automate
repetitive tasks, and reduce labor costs (Nankervis et al. 2021). The innovation
processes is a strategical practice in business companies (Bonilla-Chaves and
Palos-Sánchez 2022)
All this will result in an innovative organization full of talent with high labor
welfare, which will provide quality service to customers, obtain customer
APPLIED ARTIFICIAL INTELLIGENCE e2145631-3647

satisfaction, and lead to higher productivity. It has also been observed that the use
of AI helps predict staff turnover to avoid the reduction in productivity derived
from it.
Theoretical Contributions
Our study contributes significantly to the literature on IA and HRM implica-
tions. It is noteworthy how we introduce the framework of previous research
on AI and HRM. Through the results obtained by applying the methodology
of bibliometric analysis and systematic review of the literature, it has been
possible to ascertain the relative and insufficient attention by the academy to
these two phenomena together.
Given the lack of similar studies applying bibliometric analysis in this field
of study, it can be the first starting point on the same. This will help future
researchers as a reference point for expanding and developing the content of
this study. It can also be useful for those in HR who want to investigate and
learn more about the subject and analyze the current situation to have
a minimum number of references in case they want to enter this world.
Practical Contributions
Also important are the practical implications derived from the results of this
work for the management and administration of organizations, specifically for
HRM. The results obtained provide some very important ideas that can be of
great use to HR managers and experts related to the area to understand what
the main behaviors and trends have been so far when companies adopt HRM
connected to IA.
As noted Pan et al. (2022), an important fact is a need for managers of
organizations to encourage the development and implementation of specific
resources in the field of AI in such a way that the adoption of AI in the
company is favored.
Limitations and Future Research
This research work, like others, is also subject to a series of limitations. The
main limitation has been marked by the dispersion of information and, some-
times, limited to particular issues that do not favor a general view of the topics
in a connected way.
Research Agenda
Regarding the main lines of research derived from this work, it is important to
highlight the relevance of conducting studies that focus not only on the
e2145631-3648 P. R. PALOS-SÁNCHEZ ET AL.

application of AI in the recruitment and selection of personnel, but also on the
rest of the areas in HR management. It would also be opportune to conduct
studies that analyze AI’s effect on HRM in the employees of organizations.
Conclusions
The most relevant conclusions derived from the results obtained and their
analyses are:
First, there has been an extraordinary development in technology in recent
years, especially AI. Despite its development, importance of its impact in the
HRM field has not been as expected. AI application in HRM is a very specific
field of study, since most of the research has focused on its application in the
recruitment and selection of personnel, besides important functions like training,
development, or personnel rotation. There is indeed an increasing interest in
talent and the recruitment of highly qualified personnel, which is necessary for
facing the changing environment and high competition. But it should be noted
that talent must not only be found, but also maintained and developed to turn it
into a competitive advantage. For this reason, it is essential to use AI technologies
in other functions and extract the maximum added value from each process.
Second, based on the results obtained, it can be seen that there are still fears
and negative feelings in HR employees and managers about the AI application.
These feelings can complicate or slow down the use of AI in this area.
Although technology has strongly disrupted the labor market and has helped
create new businesses and develop existing ones, it has also eliminated many
others, thus causing greater concern. But it should be noted that AI technol-
ogies need people for their proper management. Despite being faster, working
24 hours a day, optimizing time and tasks, etc., AI does not have the essential
soft skills for any work environment.
Like any new technology, AI has its strengths and weaknesses. This makes it
essential for HR departments to carry out an effective AI implementation
strategy to integrate it safely within organizations, thus eliminating the poten-
tial damage. It is obvious that in the long term, the use of disruptive technol-
ogies will no longer be optional but rather necessary to remain competitive
among other organizations; otherwise, they will lose their market positions or
worse, will disappear.
Disclosure Statement
No potential conflict of interest was reported by the author(s).
ORCID
P.R. Palos-Sánchez http://orcid.org/0000-0001-9966-0698
APPLIED ARTIFICIAL INTELLIGENCE e2145631-3649

P. Baena-Luna http://orcid.org/0000-0002-8509-0222
J.C. Infante-Moro http://orcid.org/0000-0003-0239-5053
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