Contemporary Readings in Law and Social Justice 13(2), 2023

pp. 9–25, ISSN 1948-9137, eISSN 2162-2752

Generative Artificial Intelligence Algorithms in Talent

and Performance Management, Job Displacement and
Creation, and Employee Productivity and Well-Being

Doina Popescu Ljungholm1 and Viorica Popescu1

ABSTRACT. The objective of this paper is to systematically review generative

artificial intelligence substituting both human labor and decisions. The findings and
analyses highlight that people analytics and machine learning algorithms can be
pivotal in generative artificial intelligence-driven human resource and sustainable
organizational development and systems in terms of job and employee sentiment
analysis and monitoring. Throughout May 2023, a quantitative literature review of
the Web of Science, Scopus, and ProQuest databases was performed, with search
terms including “generative artificial intelligence algorithms” + “talent and
performance management,” “job displacement and creation,” and “employee
productivity and well-being.” As research published in 2023 was inspected, only
172 articles satisfied the eligibility criteria. By taking out controversial or ambiguous
findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too
general material, or studies with nearly identical titles, we selected 30 mainly
empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and
VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA.
Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR,
and SRDR.

Keywords: generative artificial intelligence algorithms; talent and performance

management; job displacement and creation; employee productivity and well-being

How to cite: Popescu Ljungholm, D., and Popescu, V. (2023). “Generative Artificial Intel-
ligence Algorithms in Talent and Performance Management, Job Displacement and Creation,
and Employee Productivity and Well-Being,” Contemporary Readings in Law and Social
Justice 15(2): 9–25. doi: 10.22381/CRLSJ15220231.
Received 29 June 2023 • Received in revised form 22 September 2023
Accepted 28 September 2023 • Available online 30 September 2023

1
Politehnica București National University for Science and Technology, Bucharest, Romania,
popescudoina2@gmail.com (corresponding author).
1
Politehnica București National University for Science and Technology, Bucharest, Romania,
avocat.vioricapopescu@gmail.com.
9
1. Introduction
Performance assessment related to selection, recruitment, hiring, promotion,
and sacking, team- and organization-level results develop on generative
artificial intelligence tools by use of supervised and reinforcement machine
learning techniques. The purpose of our systematic review is to examine
the recently published literature on generative artificial intelligence algo-
rithms and integrate the insights it configures on talent and performance
management, job displacement and creation, and employee productivity and
well-being. By analyzing the most recent (2023) and significant (Web of
Science, Scopus, and ProQuest) sources, our paper has attempted to prove
that generative artificial intelligence algorithms can create significant
economic gains and raise labor productivity (Andronie et al., 2023; Lăzăroiu
et al., 2017; Nica, 2018; Peters et al., 2023), while displacing employees
particularly in the services sectors through organizational effectiveness and
performance. The actuality and novelty of this study are articulated by
addressing generative artificial intelligence tools shaping knowledge work
and team structure, augmenting human agency, and improving workflow
coordination (Balcerzak et al., 2022; Lăzăroiu et al., 2022a; Nica et al.,
2023), that is an emerging topic involving much interest. Our research
problem is whether people analytics and machine learning algorithms can be
pivotal in generative artificial intelligence-driven human resource and
sustainable organizational development and systems (Cegarra Navarro et al.,
2023; Lăzăroiu et al., 2022b; Popescu et al., 2017a) in terms of job and
employee sentiment analysis and monitoring.
In this review, prior findings have been cumulated indicating that
generative artificial intelligence-based industrial automation and workforce
(Dabija et al., 2022; Lewkowich, 2022; Popescu et al., 2017b; Vătămănescu
et al., 2020) further labor market returns and productivity enhancement, job
task complexity, demand characteristics, and attributes. The identified gaps
advance generative artificial intelligence substituting both human labor and
decisions. Our main objective is to indicate that generative artificial intel-
ligence-related tasks and production value chain impact labor division and
conditions (Kliestik et al., 2020; Morley, 2022a; Popescu, 2018; Watson,
2022), business decisions, and job service automation and displacement.

2. Theoretical Overview of the Main Concepts

Memorable personalized experience data and data-driven creativity based on
extended, meaningful, and deep digital connections, sustainability messaging,
targeted promotions, exclusive offers, and discount strategies can increase
conversion rates and optimize customer journey. Generative artificial intel-
ligence systems integrate job role analysis, training history, data manage-
10
ment and analytics, and performance data, tracking employee productivity
and well-being, providing assistance and resources. The manuscript is
organized as following: theoretical overview (sectio n 2), methodology
(section 3), generative artificial intelligence-based industrial automation,
workforce labor market returns, and productivity enhancement (section 4),
immersive generative artificial intelligence technologies, job displacement
and creation, and workflow coordination (section 5), generative artificial
intelligence-driven human resource and sustainable organizational develop-
ment and systems (section 6), discussion (section 7), synopsis of the main
research outcomes (section 8), conclusions (section 9), limitations, impli-
cations, and further directions of research (section 10).

3. Methodology
Throughout May 2023, a quantitative literature review of the Web of Science,
Scopus, and ProQuest databases was performed, with search terms including
“generative artificial intelligence algorithms” + “talent and performance
management,” “job displacement and creation,” and “employee productivity
and well-being.” As research published in 2023 was inspected, only 172
articles satisfied the eligibility criteria. By taking out controversial or
ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated
by replication, too general material, or studies with nearly identical titles, we
selected 30 mainly empirical sources (Tables 1 and 2). Data visualization
tools: Dimensions (bibliometric mapping) and VOSviewer (layout algo-
rithms). Reporting quality assessment tool: PRISMA. Methodological
quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and
SRDR (Figures 1–6).
Table 1 Topics and types of scientific products identified and selected.
Topic Identified Selected
generative artificial intelligence algorithms + 59 11
talent and performance management
generative artificial intelligence algorithms + 57 10
job displacement and creation
generative artificial intelligence algorithms + 56 9
employee productivity and well-being
Type of paper
Original research 149 26
Review 12 4
Conference proceedings 9 0
Book 0 0
Editorial 2 0
Source: Processed by the authors. Some topics overlap.

11
Figure 1 Co-authorship

Figure 2 Citation

12
Figure 3 Bibliographic coupling

Figure 4 Co-citation

13
Table 2 General synopsis of evidence as regards focus topics
and descriptive outcomes (research findings)
Generative artificial intelligence can substitute Agrawal, 2023; Budhwar
both human labor and decisions, while having et al., 2023; Fosso Wamba
a significantly assistive function, shaping et al., 2023; Lee et al.,
employee wellbeing and engagement in 2023; Zhong et al., 2023
relation to job satisfaction.
Generative artificial intelligence algorithms further Agrawal et al., 2023;
labor augmentation and market disruption, Bilgram and Laarmann,
disproportionate productivity improvements, task 2023; Kunz and Wirtz,
automation, and increased income inequality. 2023; Li and Zhao, 2023
Machine learning algorithms and remote sensing Bouschery et al., 2023;
technologies can optimize manufacturing efficiency Du et al., 2023; Liu et
and productivity and predictive maintenance by al., 2023; Plathottam
assessing data acquisition, quality assurance, and et al., 2023
human resource performance.
Generative artificial intelligence tools can shape Branikas et al., 2023;
knowledge work and team structure, augment human Budhwar et al., 2023;
agency, and improve workflow coordination in an et al., 2023; Ritala
relation to business model innovation management et al., 2023
and product development.
Generative artificial intelligence can automate Chou and Lee, 2023;
routine tasks, determine the unemployment rate and Guliyev, 2023; Le Ludec
competitiveness, increase productivity and et al., 2023; Park et al.,
profitability, and drive economic growth. 2023
Competence analytics, corporate performance and Budhwar et al., 2023;
decision-making process evaluation, data governance Chang and Ke, 2023;
monitoring, and employee recruiting, onboarding, Chun et al., 2023; Que
coaching, and training develop on generative et al., 2023
artificial intelligence governance and management.
Generative artificial intelligence-based Budhwar et al., 2023;
algorithmic assistance can be pivotal in Harle, 2023; Ritala et al.,
efficient and scalable business value 2023; Saranya et al., 2023
creation and fine-tuning contextual tasks.
Generative artificial intelligence tools and Bouschery et al., 2023;
autonomous machines augment innovation team Budhwar et al., 2023;
performance, knowledge-based practices, and Jin et al., 2023; Sun
creative product development processes and tasks. et al., 2023
Generative artificial intelligence can simultaneously Budhwar et al., 2023;
deskill and destroy jobs, but organizations can Chang and Ke, 2023;
leverage its capacity to create value, improving Ko et al., 2023;
people management in terms of talent attraction, Wörsdörfer, 2023;
motivation, and retention. Zhu et al., 2023

14
 Identification
Records identified through Records identified through
Web of Science search Scopus and ProQuest search
(n = 113) (n = 172)

Records after duplicates removed

(n = 172)
Screening

Records screened Records excluded

(n = 172) (n = 41)
Eligibility

Full-text articles Full-text articles

assessed for eligibility excluded, with reasons
(n = 131) (n = 101):

Out of scope (n = 35),

Insufficient detail (n = 34),
Limited rigor (n = 32)
Included

Studies included in
qualitative synthesis
(n = 30)

Figure 5 PRISMA flow diagram describing the search results and screening.

Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA)

guidelines were used that ensure the literature review is comprehensive, transparent,
and replicable. The flow diagram, produced by employing a Shiny app, presents
the stream of evidence-based collected and processed data through the various steps
of a systematic review, designing the amount of identified, included,
and removed records, and the justifications for exclusions.
To ensure compliance with PRISMA guidelines, a citation software was used,
and at each stage the inclusion or exclusion of articles was tracked by use of custom
spreadsheet. Justification for the removal of ineligible articles was specified during
the full-text screening and final selection.

15
To ensure first-rate standard of evidence, a systematic search
of relevant databases including peer-reviewed published
journal articles was conducted using predefined search terms,
covering a range of research methods and data sources.
Reference lists of all relevant sources were manually
reviewed for additional relevant citations.

Titles of papers and abstracts were screened for suitability

and selected full texts were retrieved to establish whether
they satisfied the inclusion criteria. All records from each
database were evaluated by using data extraction forms.
Data covering research aims, participants, study design,
and method of each paper were extracted.

The inclusion criteria were: (i) articles included in the Web of

Science, Scopus, and ProQuest databases, (ii) publication date
(2023), (iii) written in English, (iv) being an original
empirical research or review article, and (v) particular search
terms covered; (i) conference proceedings, (ii) books, and (iii)
editorial materials were eliminated from the analysis.

SRDR gathered, handled, and analyzed the data for

the systematic review, being configured as an archive and tool
harnessed in data extraction through transparent, efficient, and
reliable quantitative techniques. Elaborate extraction forms
can be set up, meeting the needs of research questions and
study designs.

Distiller SR screened and extracted the collected data.

AMSTAR evaluated the methodological quality

of systematic reviews.

Dedoose analyzed qualitative and mixed methods research.

ROBIS assessed the risk of bias in systematic reviews.

AXIS evaluated the quality of cross-sectional studies.

The quality of academic articles was determined and risk of

bias was measured by MMAT, that tested content validity and
usability of selected studies in terms of screening questions,
type of design, corresponding quality criteria, and overall
quality score.
Figure 6 Screening and quality assessment tools

16
4. Generative Artificial Intelligence-based Industrial Automation,
Workforce Labor Market Returns, and Productivity Enhancement
Generative artificial intelligence can substitute both human labor and decisions
(Agrawal, 2023; Budhwar et al., 2023; Fosso Wamba et al., 2023; Lee et al.,
2023; Zhong et al., 2023), while having a significantly assistive function,
shaping employee wellbeing and engagement in relation to job satisfaction by
articulating human resource management systems, planning, and knowledge
sharing. Generative artificial intelligence technologies can reconfigure
the business landscape in relation to behavioral patterns of operational
performance, knowledge creation sharing, organizational learning processes,
ethical practices, and supply chain management in terms of increased
flexibility efficiency, and responsiveness.
Generative artificial intelligence algorithms further labor augmentation
and market disruption (Agrawal et al., 2023; Bilgram and Laarmann, 2023;
Kunz and Wirtz, 2023; Li and Zhao, 2023), disproportionate productivity
improvements, task automation, and increased income inequality. Memo-
rable personalized experience data and data-driven creativity based on ex-
tended, meaningful, and deep digital connections, sustainability messaging,
targeted promotions, exclusive offers, and discount strategies can increase
conversion rates and optimize customer journey.
Machine learning algorithms and remote sensing technologies can opti-
mize manufacturing efficiency and productivity and predictive maintenance
(Bouschery et al., 2023; Du et al., 2023; Liu et al., 2023; Plathottam et al.,
2023) by assessing data acquisition, quality assurance, and human resource
performance. Generative artificial intelligence-based industrial automation
and workforce further labor market returns and productivity enhancement,
job task complexity, demand characteristics, and attributes. (Table 3)
Table 3 Synopsis of evidence as regards focus topics and descriptive outcomes
(research findings)
Generative artificial intelligence can substitute Agrawal, 2023; Budhwar
both human labor and decisions, while having et al., 2023; Fosso Wamba
a significantly assistive function, shaping et al., 2023; Lee et al.,
employee wellbeing and engagement in 2023; Zhong et al., 2023
relation to job satisfaction.
Generative artificial intelligence algorithms further Agrawal et al., 2023;
labor augmentation and market disruption, Bilgram and Laarmann,
disproportionate productivity improvements, task 2023; Kunz and Wirtz,
automation, and increased income inequality. 2023; Li and Zhao, 2023
Machine learning algorithms and remote sensing Bouschery et al., 2023;
technologies can optimize manufacturing efficiency Du et al., 2023; Liu et
and productivity and predictive maintenance by al., 2023; Plathottam
assessing data acquisition, quality assurance, and et al., 2023
human resource performance.
17
5. Immersive Generative Artificial Intelligence Technologies,
Job Displacement and Creation, and Workflow Coordination
Generative artificial intelligence tools can shape knowledge work and team
structure, augment human agency, and improve workflow coordination in
relation to business model innovation management and product development
(Branikas et al., 2023; Budhwar et al., 2023; Pan et al., 2023; Ritala et al.,
2023) by use of photorealistic synthetic imagery and immersive simulation
training systems. Immersive generative artificial intelligence technologies
can lead both to job displacement and creation, while simultaneously shifting
human labor and creating perpetual uncertainty and psychological pressures
and distress for employees.
Generative artificial intelligence can automate routine tasks, determine
the unemployment rate and competitiveness, increase productivity and
profitability, drive economic growth, and configure creative job roles (Chou
and Lee, 2023; Guliyev, 2023; Le Ludec et al., 2023; Park et al., 2023)
through critical thinking, data-driven business analytics, and innovative
problem-solving. Generative artificial intelligence-related tasks and production
value chain impact labor division and conditions, business decisions, and job
service automation and displacement.
Competence analytics, corporate performance and decision-making process
evaluation, data governance monitoring, and employee recruiting, onboarding,
coaching, and training (Budhwar et al., 2023; Chang and Ke, 2023; Chun et
al., 2023; Que et al., 2023) develop on generative artificial intelligence
governance and management. Performance assessment related to selection,
recruitment, hiring, promotion, and sacking, team- and organization-level
results develop on generative artificial intelligence tools by use of supervised
and reinforcement machine learning techniques. (Table 4)
Table 4 Synopsis of evidence as regards focus topics and descriptive outcomes
(research findings)
Generative artificial intelligence tools can shape Branikas et al., 2023;
knowledge work and team structure, augment human Budhwar et al., 2023;
agency, and improve workflow coordination in an et al., 2023; Ritala
relation to business model innovation management et al., 2023
and product development.
Generative artificial intelligence can automate Chou and Lee, 2023;
routine tasks, determine the unemployment rate and Guliyev, 2023; Le Ludec
competitiveness, increase productivity and et al., 2023; Park et al.,
profitability, and drive economic growth. 2023
Competence analytics, corporate performance and Budhwar et al., 2023;
decision-making process evaluation, data governance Chang and Ke, 2023;
monitoring, and employee recruiting, onboarding, Chun et al., 2023; Que
coaching, and training develop on generative et al., 2023
artificial intelligence governance and management.
18
6. Generative Artificial Intelligence-driven Human Resource
and Sustainable Organizational Development and Systems
Generative artificial intelligence-based algorithmic assistance can be pivotal
in efficient and scalable business value creation and fine-tuning contextual
tasks, resulting in significant productivity gains and development processes
(Budhwar et al., 2023; Harle, 2023; Ritala et al., 2023; Saranya et al., 2023)
through machine learning-based automation systems and the Internet of
Robotic Things. Generative artificial intelligence systems integrate job role
analysis, training history, data management and analytics, and performance
data, tracking employee productivity and well-being, providing assistance
and resources.
Generative artificial intelligence tools and autonomous machines augment
innovation team performance, knowledge-based practices, and creative
product development processes and tasks in business operations (Bouschery
et al., 2023; Budhwar et al., 2023; Jin et al., 2023; Sun et al., 2023) through
reinforcement learning techniques. Generative artificial intelligence algorithms
can create significant economic gains and raise labor productivity, while dis-
placing employees particularly in the services sectors through organizational
effectiveness and performance.
Generative artificial intelligence can simultaneously deskill and destroy
jobs, but organizations can leverage its capacity to create value (Budhwar et
al., 2023; Chang and Ke, 2023; Ko et al., 2023; Wörsdörfer, 2023; Zhu et al.,
2023), improving people management in terms of talent attraction, motivation,
and retention, increasing job insecurity and precarity, and reducing labor
turnover and absence. People analytics and machine learning algorithms can
be pivotal in generative artificial intelligence-driven human resource and
sustainable organizational development and systems in terms of job and
employee sentiment analysis and monitoring. (Table 5)
Table 5 Synopsis of evidence as regards focus topics and descriptive outcomes
(research findings)
Generative artificial intelligence-based Budhwar et al., 2023;
algorithmic assistance can be pivotal in Harle, 2023; Ritala et al.,
efficient and scalable business value 2023; Saranya et al., 2023
creation and fine-tuning contextual tasks.
Generative artificial intelligence tools and Bouschery et al., 2023;
autonomous machines augment innovation team Budhwar et al., 2023;
performance, knowledge-based practices, and Jin et al., 2023; Sun
creative product development processes and tasks. et al., 2023
Generative artificial intelligence can simultaneously Budhwar et al., 2023;
deskill and destroy jobs, but organizations can Chang and Ke, 2023;
leverage its capacity to create value, improving Ko et al., 2023;
people management in terms of talent attraction, Wörsdörfer, 2023;
motivation, and retention. Zhu et al., 2023
19
7. Discussion
We integrate our systematic review throughout research indicating how
immersive generative artificial intelligence technologies can lead both to job
displacement and creation, while simultaneously shifting human labor and
creating perpetual uncertainty and psychological pressures and distress for
employees. Our research complements recent analyses clarifying how gen-
erative artificial intelligence algorithms can create significant economic
gains and raise labor productivity, while displacing employees particularly in
the services sectors through organizational effectiveness and performance.
We elucidate, by cumulative evidence, previous research demonstrating how
generative artificial intelligence-based industrial automation and workforce
further labor market returns and productivity enhancement, job task com-
plexity, demand characteristics, and attributes.

8. Synopsis of the Main Research Outcomes

Generative artificial intelligence-related tasks and production value chain
impact labor division and conditions, business decisions, and job service
automation and displacement. People analytics and machine learning algo-
rithms can be pivotal in generative artificial intelligence-driven human re-
source and sustainable organizational development and systems in terms of
job and employee sentiment analysis and monitoring. Memorable personalized
experience data and data-driven creativity based on extended, meaningful,
and deep digital connections, sustainability messaging, targeted promotions,
exclusive offers, and discount strategies can increase conversion rates and
optimize customer journey.

9. Conclusions
Relevant research has investigated whether generative artificial intelligence
systems integrate job role analysis, training history, data management and
analytics, and performance data, tracking employee productivity and well-
being, providing assistance and resources. This systematic literature review
presents the published peer-reviewed sources covering how generative
artificial intelligence technologies can reconfigure the business landscape in
relation to behavioral patterns of operational performance, knowledge creation
sharing, organizational learning processes, ethical practices, and supply chain
management in terms of increased flexibility efficiency, and responsiveness.
The research outcomes drawn from the above analyses indicate that per-
formance assessment related to selection, recruitment, hiring, promotion, and
sacking, team- and organization-level results develop on generative artificial
intelligence tools by use of supervised and reinforcement machine learning
techniques.
20
10. Limitations, Implications, and Further Directions of Research
By analyzing only articles published in 2023 in journals indexed in the Web
of Science, Scopus, and ProQuest databases, relevant sources on generative
artificial intelligence algorithms in talent and performance management, job
displacement and creation, and employee productivity and well-being may
have been excluded. Limitations of this research comprise particular kinds of
publications (original empirical research and review articles) discounting
others (conference proceedings articles, books, and editorial materials).
The scope of our study also does not move forward the inspection of gen-
erative artificial intelligence algorithms furthering labor augmentation and
market disruption.
Subsequent analyses should develop on generative artificial intelligence-
based algorithmic assistance in efficient and scalable business value creation
and fine-tuning contextual tasks. Future research should thus investigate
generative artificial intelligence tools and autonomous machines augmenting
innovation team performance, knowledge-based practices, and creative
product development processes and tasks. Attention should be directed to
generative artificial intelligence determining the unemployment rate and
competitiveness, increasing productivity and profitability, driving economic
growth, and configuring creative job roles.

Doina Popescu Ljungholm, https://orcid.org/0000-0001-5648-8162

Viorica Popescu, https://orcid.org/0000-0003-4120-5419
Compliance with ethical standards
This article does not contain any studies with human participants or animals per-
formed by the authors. Extracting and inspecting publicly accessible files (scholarly
sources) as evidence, before the research began no institutional ethics approval was
required.
Data availability statement
All data generated or analyzed are included in the published article.
Funding information
This paper was supported by Grant GE-1420417 from the Artificially Intelligent
Algorithmic Systems Research Unit, Westminster, CO, USA. The funder had no
role in study design, data collection analysis, and interpretation, decision to submit the
manuscript for publication, or the preparation and writing of this paper.
Author contributions
All authors listed have made a substantial, direct and intellectual contribution to
the work, and approved it for publication. The authors take full responsibility for
the accuracy and the integrity of the data analysis.
Conflict of interest statement
The authors declare that the research was conducted in the absence of any commercial
or financial relationships that could be construed as a potential conflict of interest.
21
 REFERENCES

Agrawal, A., Gans, J. S., and Goldfarb, A. (2023). “Do We Want Less Automation?,”
Science 381: 155–158. doi: 10.1126/science.adh9429.
Agrawal, K. P. (2023). “Towards Adoption of Generative AI in Organizational
Settings,” Journal of Computer Information Systems. doi: 10.1080/08874417.
2023.2240744.
Andronie, M., Lăzăroiu, G., Karabolevski, O. L., Ștefănescu, R., Hurloiu, I., Dijmărescu,
A., et al. (2023). “Remote Big Data Management Tools, Sensing and Computing
Technologies, and Visual Perception and Environment Mapping Algorithms in
the Internet of Robotic Things,” Electronics 12(1): 22. doi: 10.3390/electronics
12010022.
Balcerzak, A. P., Nica, E., Rogalska, E., Poliak, M., Klieštik, T., and Sabie, O.-M.
(2022). “Blockchain Technology and Smart Contracts in Decentralized Gover-
nance Systems,” Administrative Sciences 12: 96. doi: 10.3390/admsci12030096.
Beckett, S. (2022). “Virtual Retail Algorithms, Behavioral Predictive Analytics, and
Geospatial Mapping Technologies in the Decentralized Metaverse,” Review of
Contemporary Philosophy 21: 154–170. doi: 10.22381/RCP21202210.
Bilgram, V., and Laarmann, F. (2023). “Accelerating Innovation with Generative AI:
AI-Augmented Digital Prototyping and Innovation Methods,” IEEE Engineering
Management Review 51(2): 18–25. doi: 10.1109/EMR.2023.3272799.
Bouschery, S. G., Blazevic, V., and Piller, F. T. (2023). “Augmenting Human
Innovation Teams with Artificial Intelligence: Exploring Transformer -Based
Language Models,” Journal of Product Innovation Management 40(2): 139–153.
doi: 10.1111/jpim.12656.
Branikas, E., Murray, P., and West, G. (2023). “A Novel Data Augmentation
Method for Improved Visual Crack Detection Using Generative Adversarial Net-
works,” IEEE Access 11: 22051–22059. doi: 10.1109/ACCESS.2023.3251988.
Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R.,
et al. (2023). “Human Resource Management in the Age of Generative Artificial
Intelligence: Perspectives and Research Directions on ChatGPT,” Human
Resource Management Journal. doi: 10.1111/1748-8583.12524.
Cegarra Navarro, J. G., Brătianu, C., Martinez Martinez, A., Vătămănescu, E. M.,
and Dabija, D.-C. (2023). “Creating Civic and Public Engagement by a Proper
Balance between Emotional, Rational, and Spiritual Knowledge,” Journal of
Knowledge Management. doi: 10.1108/JKM-07-2022-0532.
Chang, Y.-L., and Ke, J. (2023). “Socially Responsible Artificial Intelligence Em-
powered People Analytics: A Novel Framework Towards Sustainability,” Human
Resource Development Review. doi: 10.1177/15344843231200930.
Chou, C.-B., and Lee, C.-H. (2023). “Generative Neural Network-based Online
Domain Adaptation (GNN-ODA) Approach for Incomplete Target Domain
Data,” IEEE Transactions on Instrumentation and Measurement 72: 3508110.
doi: 10.1109/TIM.2023.3246495.
Chun, P. J., Suzuki, M., and Kato, Y. (2023). “Iterative Application of Generative
Adversarial Networks for Improved Buried Pipe Detection from Images
Obtained by Ground-Penetrating Radar,” Computer-Aided Civil and Infrastructure
Engineering. doi: 10.1111/mice.13070.

22
Dabija, D.-C., Csorba, L. M., Isac, F-L., and Rusu, S. (2022). “Building Trust toward
Sharing Economy Platforms beyond the COVID-19 Pandemic,” Electronics
11(18): 2916. doi: 10.3390/electronics11182916.
Du, Y., Jia, P., and Park, A. (2023). “The Returns to Computer Use in the Chinese
Labor Market,” China Economic Quarterly International 3(2): 132–143. doi:
10.1016/j.ceqi.2023.06.001.
Fosso Wamba, S., Queiroz, M. M., Chiappetta Jabbour, C. S., and Shi, C. (V.) (2023).
“Are Both Generative AI and ChatGPT Game Changers for 21st -Century
Operations and Supply Chain Excellence?,” International Journal of Production
Economics 265: 109015. doi: 10.1016/j.ijpe.2023.109015.
Guliyev, H. (2023). “Artificial Intelligence and Unemployment in High -Tech
Developed Countries: New Insights from Dynamic Panel Data Model,” Research
in Globalization 7: 100140. doi: 10.1016/j.resglo.2023.100140.
Harle, S. M. (2023). “Advancements and Challenges in the Application of Artificial
Intelligence in Civil Engineering: A Comprehensive Review,” Asian Journal of
Civil Engineering. doi: 10.1007/s42107-023-00760-9.
Jin, K.-H., Kang, K.-S., Shin, B.-K., Kwon, J.-H., Jang, S.-J., Kim, Y.-B., et al.
(2023). “Development of Robust Detector Using the Weather Deep Generative
Model for Outdoor Monitoring System,” Expert Systems with Applications 234:
120984. doi: 10.1016/j.eswa.2023.120984.
Kliestik, T., Valaskova, K., Lăzăroiu, G., Kovacova, M., and Vrbka, J. (2020). “Re-
maining Financially Healthy and Competitive: The Role of Financial Predictors,”
Journal of Competitiveness 12(1): 74–92. doi: 10.7441/joc.2020.01.05.
Ko, J., Ennemoser, B., Yoo, W., Yan, W., and Clayton, M. J. (2023). “Architectural
Spatial Layout Planning Using Artificial Intelligence,” Automation in Construction
154: 105019. doi: 10.1016/j.autcon.2023.105019.
Kunz, W. H., and Wirtz, J. (2023). “Corporate Digital Responsibility (CDR) in
the Age of AI: Implications for Interactive Marketing,” Journal of Research in
Interactive Marketing. doi: 10.1108/JRIM-06-2023-0176.
Lăzăroiu, G., Pera, A., Ștefănescu-Mihăilă, R. O., Mircică, N., and Neguriță, O.
(2017). “Can Neuroscience Assist Us in Constructing Better Patterns of
Economic Decision-Making?,” Frontiers in Behavioral Neuroscience 11: 188.
doi: 10.3389/fnbeh.2017.00188.
Lăzăroiu, G., Androniceanu, A., Grecu, I., Grecu, G., and Neguriță, O. (2022a ).
“Artificial Intelligence-based Decision-Making Algorithms, Internet of Things
Sensing Networks, and Sustainable Cyber-Physical Management Systems in Big
Data-driven Cognitive Manufacturing,” Oeconomia Copernicana 13(4): 1045–
1078. doi: 10.24136/oc.2022.030.
Lăzăroiu, G., Andronie, M., Iatagan, M., Geamănu, M., Ștefănescu, R., and Dijmă-
rescu, I. (2022b). “Deep Learning-Assisted Smart Process Planning, Robotic
Wireless Sensor Networks, and Geospatial Big Data Management Algorithms in
the Internet of Manufacturing Things,” ISPRS International Journal of
GeoInformation 11(5): 277. doi: 10.3390/ijgi11050277.
Lee, J. A., Park, J., Choi, Y. T., Kim, R. E., Jung, J., Lee, S., et al. (2023). “Influence
of Tensile Properties on Hole Expansion Ratio Investigated Using a Generative
Adversarial Imputation Network with Explainable Artificial Intelligence,” Journal
of Materials Science 58: 4780–4794. doi: 10.1007/s10853-023-08315-8.

23
Le Ludec, C., Cornet, M., and Casilli, A. A. (2023). “The Problem with Annotation.
Human Labour and Outsourcing between France and Madagascar,” Big Data &
Society 10(2). doi: 10.1177/20539517231188723.
Lewkowich, D. (2022). “ASMR Literacies: Toward a Posthuman Structure of Feel-
ing,” Knowledge Cultures 10(2): 123–144. doi: 10.22381/kc10220226.
Li, S., and Zhao, X. (2023). “High-Resolution Concrete Damage Image Synthesis
Using Conditional Generative Adversarial Network,” Automation in Construction
147: 104739. doi: 10.1016/j.autcon.2022.104739.
Liu, P., Qi, H., Liu, J., Feng, L., Li, D., and Guo, J. (2023). “Automated Clash
Resolution for Reinforcement Steel Design in Precast Concrete Wall Panels via
Generative Adversarial Network and Reinforcement Learning,” Advanced
Engineering Informatics 58: 102131. doi: 10.1016/j.aei.2023.102131.
Mircică, N. (2022). “Immersive and Engaging Digital Content, Data Visualization
Tools, and Location Analytics in a Decentralized Metaverse,” Linguistic and
Philosophical Investigations 21: 89–104. doi: 10.22381/lpi2120226.
Morley, N. (2022a). “Autonomous Vehicle Ethics in Networked Transport Systems:
Spatial Cognition Algorithms, Mobility Data Processing Tools, and Deep
Learning-based Sensing Technologies,” Contemporary Readings in Law and
Social Justice 14(2): 82–99. doi: 10.22381/CRLSJ14220225.
Morley, N. (2022b). “Perception and Cognition Algorithms, Simulation Modeling and
Data Visualization Tools, and Spatial Computing and Immersive Technologies
in the Metaverse Interactive Environment,” Analysis and Metaphysics 21: 244–
260. doi: 10.22381/am21202215.
Nica, E. (2018). “The Social Concretisation of Educational Postmodernism,”
Educational Philosophy and Theory 50(14): 1659–1660. doi: 10.1080/0013
1857.2018.1461364.
Nica, E., Popescu, G. H., Poliak, M., Kliestik, T., and Sabie, O.-M. (2023). “Digital
Twin Simulation Tools, Spatial Cognition Algorithms, and Multi-Sensor Fusion
Technology in Sustainable Urban Governance Networks,” Mathematics 11(9):
1981. doi: 10.3390/math11091981.
Pan, Y., Zhang, Y., Wang, X., Gao, X. X., and Hou, Z. (2023). “Low-Cost Livestock
Sorting Information Management System Based on Deep Learning,” Artificial
Intelligence in Agriculture 9: 110–126. doi: 10.1016/j.aiia.2023.08.007.
Park, N. H., Manica, M., Born, J., Hedrick, J. L., Erdmann, T., Zubarev, D. Y., et al.
(2023). “Artificial Intelligence driven Design of Catalysts and Materials for Ring
Opening Polymerization Using a Domain -specific Language,” Nature
Communications 14: 3686. doi: 10.1038/s41467-023-39396-3.
Peters, M. A., Jackson, L., Papastephanou, M., Jandrić, P., Lăzăroiu, G., Evers, C.
W., et al. (2023). “AI and the Future of Humanity: ChatGPT-4, Philosophy and
Education – Critical Responses,” Educational Philosophy and Theory. doi:
10.1080/00131857.2023.2213437.
Plathottam, S. J., Rzonca, A., Lakhnori, R., and Iloeje, C. O. (2023). “A Review of
Artificial Intelligence Applications in Manufacturing Operations,” Journal of
Advanced Manufacturing and Processing 5(3): e10159. doi: 10.1002/amp2.10159.
Popescu, G. H., Nica, E., Ciurlău, F. C., Comănescu, M., and Bițoiu, T. (2017a).
“Stabilizing Valences of an Optimum Monetary Zone in a Resilient Economy –
Approaches and Limitations,” Sustainability 9(6): 1051. doi: 10.3390/su9061051.

24
Popescu, G. H., Sima, V., Nica, E., and Gheorghe, I. G. (2017b). “Measuring Sustain-
able Competitiveness in Contemporary Economies – Insights from European
Economy,” Sustainability 9(7): 1230. doi: 10.3390/su9071230.
Popescu, G. H. (2018). “Has Postmodernism the Potential to Reshape Educational
Research and Practice?,” Educational Philosophy and Theory 50(14): 1490–
1491. doi: 10.1080/00131857.2018.1461376.
Popescu, G. H., Valaskova, K., and Horak, J. (2022). “Augmented Reality Shopping
Experiences, Retail Business Analytics, and Machine Vision Algorithms in
the Virtual Economy of the Metaverse,” Journal of Self-Governance and
Management Economics 10(2): 67–81. doi: 10.22381/jsme10220225.
Que, Y., Dai, Y., Ji, X., Kwan Leung, A., Chen, Z., Jiang, Z., et al. (2023).
“Automatic Classification of Asphalt Pavement Cracks Using a Novel Integrated
Generative Adversarial Networks and Improved VGG Model,” Engineering
Structures 277: 115406. doi: 10.1016/j.engstruct.2022.115406.
Ritala, P., Ruokonen, M., and Ramaul, L. (2023). “Transforming Boundaries: How
Does ChatGPT Change Knowledge Work?,” Journal of Business Strategy. doi:
10.1108/JBS-05-2023-0094.
Saranya, T., Deisy, C., Sridevi, S., and Anbananthen, K. S. M. (2023). “A Com-
parative Study of Deep Learning and Internet of Things for Precision Agri-
culture,” Engineering Applications of Artificial Intelligence 122: 106034. doi:
10.1016/j.engappai.2023.106034.
Sun, K., Wang, W., Cheng, R., Liang, Y., Xie, H., Wang, J., et al. (2023). “Evo-
lutionary Generative Design of Supercritical Airfoils: An Automated Approach
Driven by Small Data,” Complex & Intelligent Systems. doi: 10.1007/s40747-
023-01214-0.
Vătămănescu, E.-M., Alexandru, V.-A., Mitan, A., and Dabija, D.-C. (2020). “From
the Deliberate Managerial Strategy towards International Business Performance:
A Psychic Distance vs. Global Mindset Approach,” Systems Research and
Behavioral Science 37(2): 374–387. doi: 10.1002/sres.2658.
Watson, R. (2022). “Tradeable Digital Assets, Immersive Extended Reality Tech-
nologies, and Blockchain-based Virtual Worlds in the Metaverse Economy,”
Smart Governance 1(1): 7–20. doi: 10.22381/sg1120221.
Wörsdörfer, M. (2023). “The E.U.’s Artificial Intelligence Act: An Ordoliberal
Assessment,” AI Ethics. doi: 10.1007/s43681-023-00337-x.
Zhong, J., Huyan, J., Zhang, W., Cheng, H., Zhang, J., Tong, Z., et al. (2023).
“A Deeper Generative Adversarial Network for Grooved Cement Concrete Pave-
ment Crack Detection,” Engineering Applications of Artificial Intelligence 119:
105808. doi: 10.1016/j.engappai.2022.105808.
Zhu, X., Lou, Y., Zhao, J., Gao, W., and Deng, H. (2023). “Generative Non-
Autoregressive Unsupervised Keyphrase Extraction with Neural Topic Modeling,”
Engineering Applications of Artificial Intelligence 120: 105934. doi: 10.1016/
j.engappai.2023.105934.

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 Contemporary Readings in Law and Social Justice 15(2), 2023
pp. 26–45, ISSN 1948-9137, eISSN 2162-2752

Immersive Remote Collaboration and Workplace Tracking

Systems, Mobile Biometric and Sentiment Data, and
Algorithmic Monitoring and Wearable Augmented Reality
Technologies in Generative Artificial Intelligence-based
Virtual Human Resource Management
Vladislav Krastev1, Blagovesta Koyundzhiyska-Davidkova1,
and Susan Buckley2
ABSTRACT. We draw on a substantial body of theoretical and empirical research
on workplace collaboration software, synthetic training data, and employee engage-
ment analytics. We carried out a quantitative literature review of ProQuest, Scopus,
and the Web of Science throughout May 2023, with search terms including “gener-
ative artificial intelligence-based virtual human resource management” + “immersive
remote collaboration and workplace tracking systems,” “mobile biometric and
sentiment data,” and “algorithmic monitoring and wearable augmented reality
technologies.” As we analyzed research published in 2023, only 170 papers met the
eligibility criteria, and 52 mainly empirical sources were selected. ATLAS.ti was
used in conducting a systematic review of the literature. Data visualization tools:
Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting
quality assessment tool: PRISMA. Methodological quality assessment tools include:
AMSTAR, Distiller SR, ROBIS, and SRDR.
Keywords: immersive remote collaboration; workplace tracking system; mobile
biometric and sentiment data; algorithmic monitoring; wearable augmented reality;
generative artificial intelligence; virtual human resource management
How to cite: Krastev, V., Koyundzhiyska-Davidkova, B., and Buckley, S. (2023). “Immersive
Remote Collaboration and Workplace Tracking Systems, Mobile Biometric and Sentiment Data,
and Algorithmic Monitoring and Wearable Augmented Reality Technologies in Generative
Artificial Intelligence-based Virtual Human Resource Management,” Contemporary Readings
in Law and Social Justice 15(2): 26–45. doi: 10.22381/CRLSJ15220232.
Received 22 June 2023 • Received in revised form 25 November 2023
Accepted 28 November 2023 • Available online 30 November 2023

1
Faculty of Economics, The South-West University “Neofit Rilski”, Blagoevgrad, Bulgaria,
vladislav_swu@law.swu.bg.
1
Faculty of Economics, The South-West University “Neofit Rilski”, Blagoevgrad, Bulgaria,
vesi_8808@abv.bg.
2
Digital Urban Governance Research Unit at ISBDA, Wollongong, Australia, susan.buckley@aa-
er.org. (corresponding author)
26
1. Introduction
Generative artificial intelligence and data visualization tools enable virtual
recruitment and onboarding, enhance knowledge acquisition and career
progression, and forecast job displacement. The purpose of our systematic
review is to examine the recently published literature on generative artificial
intelligence-based virtual human resource management and integrate the in-
sights it configures on immersive remote collaboration and workplace tracking
systems, mobile biometric and sentiment data, and algorithmic monitoring
and wearable augmented reality technologies. By analyzing the most recent
(2023) and significant (Web of Science, Scopus, and ProQuest) sources, our
paper has attempted to prove that employee engagement analytics (Andronie
et al., 2021a; Capestro et al., 2024; Duncan, 2022; Lăzăroiu and Rogalska,
2023), immersive haptic experiences, and hiring practices require generative
artificial intelligence and wearable augmented reality technologies. The ac-
tuality and novelty of this study are articulated by addressing long-term talent
pipelines, adaptive learning experiences (Andronie et al., 2021b; Lăzăroiu et
al., 2023; Musova et al., 2021; Popescu et al., 2017a), and employee commit-
ment and performance, that is an emerging topic involving much interest.
Our research problem is whether generative artificial intelligence and com-
puter vision algorithms can harness employee engagement analytics (Dvorsky
et al., 2020; Maroušek et al., 2020; Nica et al., 2023a), physiological and
behavioral biometrics, and realistic movement simulations (Andronie et al.,
2021; Bargoni et al., 2023; Naepi and Naepi, 2022; Popescu et al., 2017b) in
immersive work environments.
In this review, prior findings have been cumulated indicating that gen-
erative artificial intelligence and emotion recognition tools can reduce
unreasonable workload, lead to productive workplace (Andronie et al., 2023;
Nica, 2017; Peters et al., 2023; Popescu, 2018), and boost employee
engagement. The identified gaps advance workplace collaboration software
(Dabija et al., 2018; Nica, 2018; Pelau et al., 2021; Vătămănescu et al.,
2022), synthetic training data, and employee engagement analytics. Our
main objective is to indicate that generative artificial intelligence and remote
collaboration tools can assist in knowledge sharing practices, changing work-
force needs (Gaspareniene et al., 2022; Nica et al., 2023b; Poliak et al.,
2023), and career development.

2. Theoretical Overview of the Main Concepts

Generative artificial intelligence and algorithmic monitoring systems are
instrumental in effective organizational practices, workforce motivation and
commitment, and labor productivity growth. Data-driven cognitive and affective
processes, organizational performance practices, and employee effectiveness
and productivity necessitate generative artificial intelligence and voice and
27
gesture recognition technologies. The manuscript is organized as following:
theoretical overview (section 2), methodology (section 3), generative artificial
intelligence and deep learning computer vision algorithms, employee engage-
ment analytics, and body-tracking data metrics in virtual workplaces (section
4), generative artificial intelligence and algorithmic tracking technologies,
immersive haptic experiences, and remote collaboration tools in virtual
workspaces and meetings (section 5), generative artificial intelligence and
algorithmic monitoring systems, performance management processes, and
employee satisfaction and engagement in immersive work environments
(section 6), discussion (section 7), synopsis of the main research outcomes
(section 8), conclusions (section 9), limitations, implications, and further
directions of research (section 10).

3. Methodology
We carried out a quantitative literature review of ProQuest, Scopus, and
the Web of Science throughout May 2023, with search terms including “gen-
erative artificial intelligence-based virtual human resource management” +
“immersive remote collaboration and workplace tracking systems,” “mobile
biometric and sentiment data,” and “algorithmic monitoring and wearable
augmented reality technologies.” As we analyzed research published in
2023, only 170 papers met the eligibility criteria, and 52 mainly empirical
sources were selected (Tables 1 and 2). ATLAS.ti was used in conducting a
systematic review of the literature. Data visualization tools: Dimensions
(bibliometric mapping) and VOSviewer (layout algorithms). Reporting
quality assessment tool: PRISMA. Methodological quality assessment tools
include: AMSTAR, Distiller SR, ROBIS, and SRDR (Figures 1–6).
Table 1 Topics and types of scientific products identified and selected.
Topic Identified Selected
generative artificial intelligence-based virtual human 59 19
resource management + immersive remote collaboration
and workplace tracking systems
generative artificial intelligence-based virtual human 57 17
resource management + mobile biometric and
sentiment data
generative artificial intelligence-based virtual human 54 16
resource management + algorithmic monitoring and
wearable augmented reality technologies
Type of paper
Original research 138 48
Review 16 4
Conference proceedings 13 0
Book 1 0
Editorial 2 0
Source: Processed by the authors. Some topics overlap.
28
Figure 1 Co-authorship

Figure 2 Citation

29
Figure 3 Bibliographic coupling

Figure 4 Co-citation

30
Table 2 General synopsis of evidence as regards focus topics
and descriptive outcomes (research findings).
Generative artificial intelligence and Aleem et al., 2023; Cardon et al.,
suitable job-matching systems can 2023; Fosso Wamba et al., 2023;
configure long-term talent pipelines, Jang and Landuyt, 2023; Monod
adaptive learning experiences, and et al., 2023; Thakur and Kushwaha,
employee commitment and performance. 2023
Generative artificial intelligence and deep Angarano et al., 2023; Carmel and
learning computer vision algorithms can Sawyer, 2023; Garibay et al., 2023;
deploy workplace collaboration software, Kambur and Yildirim, 2023; Moon,
synthetic training data, and employee 2023; Vannuccini and Prytkova, 2023
engagement analytics.
Generative artificial intelligence and virtual Ayhan and Elal, 2023; Chen et al.,
navigation tools can leverage immersive 2023; Ghobakhloo et al., 2023;
audiovisual content, body-tracking data Koivisto and Grassini, 2023; Mpia
metrics, and network visual analytics in et al., 2023; Victor et al., 2023
virtual workplaces.
Generative artificial intelligence and adaptive Bartelheimer et al., 2023;
self-organizing systems can assess workforce Chou and Lee, 2023; Gibson
commitment, maximize performance et al., 2023; Liu, 2023; Pappas
outcomes, and enable employee turnover, et al., 2023; Wang, 2023
articulating measurable organizational goals.
Virtual workspaces and meetings, employee Bendel, 2023; Chun et al., 2023;
sentiment, engagement, and retention, and Gama and Magistretti, 2023; Liu
performance management practices can et al., 2023; Longoni et al., 2023;
integrate generative artificial intelligence Pellas, 2023; Wörsdörfer, 2023
and algorithmic tracking technologies.
Generative artificial intelligence and Böhmer and Schinnenburg, 2023;
mobile analytics algorithms can further Cortez and Maslej, 2023; Harms
virtual collaboration tasks, psychosocial et al., 2024; Luleci et al., 2023;
working conditions, and employee Prettner, 2023; Yang et al., 2023
performance parameters.
Generative artificial intelligence and Bouschery et al., 2023; Díaz-
remote sensing systems can redefine Rodríguez et al., 2023; Hassija
employment, tasks, and jobs, further et al., 2023; Malhan and Gupta,
organizational performance, and 2023; Sibilla and Gorgoni, 2023
performance management processes.
Generative artificial intelligence and Buehler, 2023; Ferrari and McKelvey,
cognitive enhancement technologies 2023; Horváth and Vicsek, 2023;
are pivotal in talent attraction and Mannuru et al., 2023; Smales, 2023
retention, in making employment decisions,
and in enhancing worker productivity.
Generative artificial intelligence and Carvalho and Ivanov, 2023; França et
natural language processing algorithms al., 2023; Ivanov and Webster, 2024;
can shape labor force participation rates, Modliński et al., 2023; Steinfeld, 2023
organizational commitment, and
employee satisfaction and engagement.
31
 Identification

Records identified through Records identified through

Web of Science search Scopus and ProQuest search
(n = 114) (n = 170)

Records after duplicates removed

(n = 170)
Screening

Records screened Records excluded

(n = 170) (n = 45)
Eligibility

Full-text articles Full-text articles

assessed for eligibility excluded, with reasons
(n = 125) (n = 73):

Out of scope (n = 24),

Insufficient detail (n = 25),
Limited rigor (n = 24)
Included

Studies included in
qualitative synthesis
(n = 52)

Figure 5 PRISMA flow diagram describing the search results and screening.

Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA)

guidelines were used that ensure the literature review is comprehensive, transparent,
and replicable. The flow diagram, produced by employing a Shiny app, presents
the stream of evidence-based collected and processed data through the various steps
of a systematic review, designing the amount of identified, included,
and removed records, and the justifications for exclusions.
To ensure compliance with PRISMA guidelines, a citation software was used,
and at each stage the inclusion or exclusion of articles was tracked by use of custom
spreadsheet. Justification for the removal of ineligible articles was specified during
the full-text screening and final selection.

32
To ensure first-rate standard of evidence, a systematic search
of relevant databases including peer-reviewed published
journal articles was conducted using predefined search terms,
covering a range of research methods and data sources.
Reference lists of all relevant sources were manually
reviewed for additional relevant citations.

Titles of papers and abstracts were screened for suitability

and selected full texts were retrieved to establish whether
they satisfied the inclusion criteria. All records from each
database were evaluated by using data extraction forms.
Data covering research aims, participants, study design,
and method of each paper were extracted.

The inclusion criteria were: (i) articles included in the Web of

Science, Scopus, and ProQuest databases, (ii) publication date
(2023), (iii) written in English, (iv) being an original
empirical research or review article, and (v) particular search
terms covered; (i) conference proceedings, (ii) books, and (iii)
editorial materials were eliminated from the analysis.

SRDR gathered, handled, and analyzed the data for

the systematic review, being configured as an archive and tool
harnessed in data extraction through transparent, efficient, and
reliable quantitative techniques. Elaborate extraction forms
can be set up, meeting the needs of research questions and
study designs.

Distiller SR screened and extracted the collected data.

AMSTAR evaluated the methodological quality

of systematic reviews.

The quality of academic articles was determined and risk of

bias was measured by MMAT, that tested content validity and
usability of selected studies in terms of screening questions,
type of design, corresponding quality criteria, and overall
quality score.

Dedoose analyzed qualitative and mixed methods research.

AXIS evaluated the quality of cross-sectional studies.

ROBIS assessed the risk of bias in systematic reviews.

Figure 6 Screening and quality assessment tools

33
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