J. Manage. Hum. Resour. (July - December 2026) 4(2):

https://doi.org/10.5281/zenodo.21652122

e-ISSN 3091-1850

ORIGINAL ARTICLE

Algorithmic human resource management and employee well-being: current

evidence from a systematic literature review

Gestión algorítmica de los recursos humanos y bienestar laboral: evidencia actual de una revisión sistemática de

la literatura

Joel Menezes Barreto Júnior

1,2

iD Duliet Hong León

1,2

iD Jaime Eliecer Pérez Fernández

1,2

iD António Ramos Congo

Mavambo

1,2

iD Natalia Águeda Domingas Chivango

1,2

Received: 02/04/26 / Accepted: 11/06/26 / Published online: 25/07/26

Abstract

This study examines the relationship between artificial intelligence

(AI), algorithmic human resource management, and employee

well-being through a systematic review of the scientific literature

published between 2020 and 2026. The review addresses the growing

need to understand how algorithm-supported decision-making

influences employee experience, psychological well-being,

organizational trust, and perceptions of fairness in contemporary

workplaces. The study followed the PRISMA 2020 guidelines and

included peer-reviewed articles retrieved from Scopus, Web of

Science, ScienceDirect, and SpringerLink. Following the

identification, screening, and eligibility stages, 82 studies were

selected for qualitative thematic analysis and descriptive synthesis. The

findings indicate that artificial intelligence enhances recruitment and

selection, performance evaluation, and human resource analytics while

simultaneously introducing challenges related to digital surveillance,

algorithmic opacity, technological anxiety, and reduced employee

autonomy. Overall, the evidence suggests that algorithmic human

resource management can promote employee well-being when

implemented within transparent governance frameworks characterized

by human oversight, algorithmic fairness, ethical data governance, and

active employee participation. The review contributes by synthesizing

current evidence, identifying major research trends and knowledge

gaps, and highlighting future research priorities, particularly for Latin

American organizational contexts, where empirical evidence remains

limited.

Keywords artificial intelligence; algorithmic human resource

management; employee well-being; human resource analytics;

algorithmic fairness.

Resumen

El presente artículo analiza la relación entre la inteligencia artificial, la

gestión algorítmica del talento humano y el bienestar laboral, a partir

de una revisión sistemática de la literatura científica publicada entre

2020 y 2026. El estudio responde a la necesidad de comprender los

efectos que los sistemas automatizados de toma de decisiones generan

sobre la experiencia, la salud psicológica y la percepción de justicia de

los trabajadores. La metodología siguió las directrices PRISMA 2020 y

consideró artículos revisados por pares recuperados de Scopus, Web of

Science, ScienceDirect y SpringerLink. Tras el proceso de

identificación, cribado y elegibilidad, se incluyeron 82 estudios para el

análisis temático y descriptivo. Los resultados evidencian que la

inteligencia artificial contribuye a optimizar procesos de reclutamiento,

selección, evaluación del desempeño y analítica de recursos humanos;

sin embargo, también genera riesgos asociados a la vigilancia digital,

la opacidad algorítmica, la ansiedad tecnológica y la pérdida de

autonomía laboral. Se concluye que la gestión algorítmica puede

favorecer el bienestar laboral cuando se implementa bajo principios de

transparencia, supervisión humana, justicia algorítmica y participación

de los trabajadores. El estudio propone fortalecer modelos de

gobernanza algorítmica centrados en las personas y ampliar la

investigación empírica en contextos latinoamericanos.

Palabras clave inteligencia artificial; gestión algorítmica; talento

humano; bienestar laboral; justicia algorítmica.

How to cite

Barreto Júnior, J. M., Hong León, D., Pérez Fernández, J. E., Ramos Congo Mavambo, A., & Domingas Chivango, N. Á. (2026). Algorithmic human resource

management and employee well-being: current evidence from a systematic literature review. Journal of Management and Human Resources, 4(2),

https://doi.org/10.5281/zenodo.21652122

Corresponding author

Joel Menezes Barreto Júnior

[email protected]

iD https://orcid.org/0000-0002-2986-7025

Universidade Internacional do Cuanza (UNIC), Cuito, Bié, Angola

FUNIBER, España

2227

ReviewArticles

REVIEWARTICLES

Barreto,J.M.,Pérez,J.E.,Congo,A.R.,León,D.H.,&Domingas,N.Á.(2026).Algorithmichumanresourcemanagementandemployeewellbeing:

currentevidencefromasystematicliteraturereview.JournalofManagementandHumanResources,4(2),

https://doi.org/10.5281/zenodo.21652122

JoelM.Barreto

1,2

iD DulietH.León

1,2

iD JaimeE.Pérez

1,2

iD AntónioR.Congo

1,2

iD NataliaÁ.Domingas

1,2

iD

Barreto, J. M., Pérez, J. E., Congo, A. R., León, D. H., & Domingas, N. Á. (2026). Algorithmic human resource management and employee wellbeing:

current evidence from a systematic literature review. Journal of Management and Human Resources, 4(2),

https://doi.org/10.5281/zenodo.21652122

Introduction

The rapid adoption of artificial intelligence (AI) is reshaping

human resource management (HRM), which is increasingly

supported by predictive analytics, machine learning, and algorithmic

decision systems. These technologies enable organizations to process

workforce data at scale and support recruitment, performance

management, workforce planning, and employee development

(Minbaeva, 2021; Strohmeier, 2020).

Human Resource Analytics has contributed to this transformation

by allowing organizations to identify workforce patterns, anticipate

turnover and performance risks, and support evidence-based

interventions. Algorithmic systems now assist HR professionals in

screening candidates, evaluating competencies, recommending

promotions, and planning staffing needs, thereby increasing the speed

and analytical capacity of HR decision-making (Meijerink et al.,

2021).

However, technological efficiency does not necessarily imply fair

or beneficial outcomes. Algorithmic systems can reproduce historical

inequalities when trained on biased data, and their opacity can

complicate accountability in employment decisions (Köchling &

Wehner, 2020; Raghavan et al., 2020). For this reason, fairness,

explainability, and human oversight have become central concerns in

algorithm-supported HRM.

Employee trust and acceptance also depend on how these

technologies are introduced. Research on algorithmic management

shows that monitoring, performance control, and automated

decision-making can affect autonomy, perceived justice, and

employee well-being, particularly when workers have limited

understanding of or influence over the systems that shape their work

(Kellogg et al., 2020; Parent-Rocheleau & Parker, 2022).

The Job Demands–Resources (JD–R) Model provides a useful

framework for interpreting these effects (Bakker & Demerouti, 2017).

AI-based technologies may operate as organizational resources when

they reduce repetitive work, improve access to information, or

support decision-making; conversely, they may become job demands

when they intensify surveillance, workload, performance pressure, or

insecurity. Empirical evidence from European organizations likewise

shows that algorithmic management can influence workplace

well-being directly and indirectly through job autonomy and reward

practices (Kinowska & Sienkiewicz, 2023).

Recent research therefore suggests that the consequences of AI

depend substantially on organizational conditions surrounding

implementation. Employee participation, transparent communication,

human oversight, and ethical data governance can mitigate risks and

strengthen trust, while opaque or highly controlling systems may

undermine autonomy and psychological well-being (Budhwar et al.,

2022; Parent-Rocheleau & Parker, 2022).

This concern has also acquired regulatory relevance. Regulation

(EU) 2024/1689, the Artificial Intelligence Act, establishes

harmonized rules for AI systems and reinforces requirements related

to risk management, transparency, accountability, and human

oversight, including in employment-related contexts (European

Parliament & Council of the European Union, 2024).

Despite rapid growth in research on AI and HRM, the evidence

remains mixed. Some studies emphasize gains in efficiency,

analytics, and employee development, whereas others identify risks

associated with surveillance, algorithmic bias, reduced autonomy,

technological anxiety, and organizational injustice (Budhwar et al.,

2022; Kim et al., 2025; Parent-Rocheleau & Parker, 2022).

These tensions are especially relevant in regions where empirical

evidence remains limited. Research on algorithmic HRM and

employee well-being is still concentrated in North America, Europe,

and parts of Asia, leaving fewer context-specific studies from Latin

America and other emerging economies. This geographical imbalance

restricts the transferability of existing conclusions and reinforces the

need for broader comparative research.

Accordingly, an integrative synthesis is needed to clarify how

algorithmic HRM affects employee well-being, identify the

organizational conditions associated with positive or negative

outcomes, and establish priorities for future research. A systematic

literature review offers an appropriate approach for organizing this

fragmented body of evidence (Page et al., 2021; Snyder, 2019).

Accordingly, the present study addresses the following research

question:

What are the main effects of algorithmic human resource

management on employee well-being reported in the recent scientific

literature?

To answer this question, the objective of this study is to

systematically analyze scientific evidence published between 2020

and 2026 on the relationship between artificial intelligence,

algorithmic human resource management, and employee well-being.

The review identifies prevailing research trends, theoretical

perspectives, reported benefits and risks, knowledge gaps, and future

research priorities for ethical, transparent, and human-centered HRM.

Methodology

This study employed a systematic literature review guided by

PRISMA 2020 (Preferred Reporting Items for Systematic Reviews

and Meta-Analyses), which supports transparent reporting of

identification, screening, eligibility, and inclusion procedures (Page et

al., 2021). No prospective protocol registration is reported in the

source documentation for this review.

The final set of 82 studies constituted the evidence base for

examining relationships among artificial intelligence, algorithmic

HRM, employee well-being, organizational trust, autonomy, fairness,

and related psychosocial outcomes.

A systematic review was appropriate because it enabled structured

comparison of evidence across organizational contexts and

methodological approaches while supporting the identification of

recurrent themes, research gaps, and future research priorities.

The literature search was conducted between January and March

2026 in Scopus, Web of Science Core Collection, ScienceDirect, and

SpringerLink.

The search strategy combined terms related to artificial

intelligence, algorithmic management, HRM, and employee

well-being. A common conceptual search structure was adapted to the

syntax available in each database. Exact platform-specific search

strings were not preserved in the source records, which is

acknowledged as a reproducibility limitation.

J.Manage.Hum.Resour.(JulyDecember2026)4(2):2227

Figure 1 summarizes the study selection process. The search identified

1,247 records; after duplicate removal, screening, and eligibility assessment,

82 peerreviewed studies published between 2020 and 2026 were retained

forqualitativethematicanalysisanddescriptivesynthesis.

The core Boolean structure was: ("Artificial Intelligence" OR "AI"

OR "Algorithmic Management" OR "Digital Human Resource

Management" OR "HR Analytics") AND ("Human Resource

Management" OR "Talent Management" OR "Human Resources")

AND ("Employee Well-being" OR "Workplace Well-being" OR

"Employee Experience" OR "Occupational Health").

Supplementary combinations of these terms were applied across

titles, abstracts, and keywords to improve sensitivity while retaining

the predefined eligibility criteria.

Inclusion Criteria

Table 1. Inclusion criteria applied in the systematic literature review

Inclusion Criterion Description

Document type Peer-reviewed journal articles.

Publication period Studies published between 2020 and 2026.

Language Articles published in English or Spanish.

Research focus

Studies addressing artificial intelligence, algorithmic management,

digital human resource management, or HR analytics in the context of

human resource management.

Outcome variables

Studies explicitly examining employee well-being, workplace

well-being, employee experience, job satisfaction, occupational health,

or related psychosocial outcomes.

Availability Full-text articles available for review and analysis.

Table 2. Exclusion criteria applied in the systematic literature review

Exclusion Criterion Description

Document type

Books, book chapters, dissertations, conference abstracts, editorials,

commentaries, letters, and review notes.

Research focus

Studies exclusively focused on technological or engineering aspects

without implications for human resource management or employee

well-being.

Duplicates Duplicate records identified across the selected databases.

Methodological relevance

Studies lacking empirical findings or relevant conceptual contributions

related to the research objective.

Accessibility Publications without full-text access.

Study Selection Process

The study selection process followed four PRISMA stages:

identification, duplicate removal, screening, and eligibility. Screening

decisions were made against the predefined inclusion and exclusion

criteria. The source records do not document formal inter-rater

agreement statistic or independent duplicate screening, and no such

reliability claim is made here.

Stage 1. Identification

The initial database search yielded 1,247 records, including 518

from Scopus, 326 from Web of Science, 241 from ScienceDirect, and

162 from SpringerLink.

Stage 2. Duplicate Removal

After bibliographic screening using reference management

software, 287 duplicate records were removed, resulting in a

preliminary dataset of 960 publications.

Stage 3. Screening

Titles and abstracts of the 960 records were screened against the

predefined inclusion criteria. During this stage, 731 records were

excluded because they did not meet the eligibility requirements.

Stage 4. Eligibility

A total of 229 full-text articles were assessed for eligibility. Of

these, 147 were excluded because they did not directly address

employee well-being, focused exclusively on technological or

engineering issues, provided insufficient methodological information,

or lacked full-text accessibility. The final review therefore comprised

82 peer-reviewed studies.

Data Extraction

A standardized data-extraction matrix recorded author(s), year,

country, journal, research objective, methodological design, type of

AI or algorithmic management examined, HRM process, employee

well-being variables, principal findings, and reported limitations. The

complete matrix containing bibliographic identification of the 82

included studies forms part of the review dataset referenced in the

Data availability statement.

The extracted information was organized in electronic spreadsheets

to support comparison, coding, categorization, and synthesis across

studies.

Methodological Quality Assessment

Methodological quality was appraised descriptively using seven

criteria: clarity of objectives, appropriateness of research design,

adequacy of sample description, transparency of data collection,

suitability of analysis, consistency of results, and coherence between

findings and conclusions. The matrix was used to inform interpretive

confidence rather than to generate a weighted quality score or

validated cut-off.

Table 3. Methodological quality assessment

Quality criterion Assessment focus

Clarity of objectives Clearly defined research objectives

Research design Appropriate methodological design

Sample description Adequate description of participants or units of analysis

Data collection Transparent procedures

Data analysis Appropriate analytical techniques

Results consistency Findings supported by the analyses

Conclusions Conclusions consistent with reported evidence

Source: Prepared by the authors based on the systematic literature

review

Studies with clearly insufficient methodological information were

excluded during eligibility. For the studies included, the seven-item

quality matrix was used as a structured descriptive appraisal; no

formal numerical threshold or inter-rater reliability coefficient is

reported.

Data Analysis

Extracted data were analyzed through qualitative thematic analysis

complemented by descriptive synthesis. Recurring concepts and

relationships were coded and grouped into broader analytical

dimensions, while descriptive summaries were used for study design,

publication year, and thematic distribution.

Table 4. Representative eligible studies from the 2020–2026 review

corpus

Author(s) Year

Country/Contex

Research Design Main Topic Key Finding

Kellogg et al. 2020 International Integrative review

Algorithmic

control at work

Algorithmic control

reshapes direction,

evaluation, and

discipline, with

implications for

worker autonomy

and control.

Raghavan et al. 2020 United States

Empirical / technical

-legal analysis

Algorithmic

hiring bias

Automated hiring

systems can

reproduce or

obscure discriminato

ry patterns when

data and validation

practices are

inadequate.

Meijerink et al. 2021 International Conceptual review

HRM and algorith

Algorithmic systems

redistribute HRM

activities and raise

questions about

human agency,

responsibility, and

governance.

Budhwar et al. 2022 International Review

AI and internation

al HRM

AI creates HRM

opportunities and

risks whose effects

depend strongly on

organizational and

institutional context.

Parent-Rocheleau &

Parker

2022 International Review

Algorithmic

management and

work design

Algorithmic

management can

alter job resources

and demands,

including autonomy,

workload, and

monitoring, with

consequences for

well-being.

Kinowska &

Sienkiewicz

2023

European organiz

ations

Quantitative

Algorithmic

management and

well-being

Algorithmic

management is

associated with

workplace well-bein

g directly and

indirectly through

job autonomy and

reward practices.

Kim et al. 2025 International Review

Strategic HRM

and algorithmic

technologies

Algorithmic

technologies reshape

work design and HR

delivery; human

agency and organizat

ional context remain

central to outcomes.

J.Manage.Hum.Resour.(JulyDecember2026)4(2):2227

Initially, the thematic analysis identified a series of emerging

categories across the selected studies, including AI-assisted

recruitment and selection, algorithmic performance evaluation,

human resource analytics, employee well-being, mental health,

algorithmic fairness, organizational transparency, employee

autonomy, and artificial intelligence ethics.

Subsequently, these categories were synthesized into broader

analytical dimensions to identify recurring patterns, dominant

research trends, and emerging challenges reported in the recent

scientific literature. This analytical process enabled the development

of a comprehensive understanding of how algorithmic human

resource management influences employee well-being while

providing an evidence-based foundation for academic discussion and

practical recommendations aimed at fostering responsible AI

implementation in organizational settings.

Results and discussion

Table 5. General characteristics of the included studies

Characteristic Frequency Percentage (%)

Quantitative studies 37 45.1

Qualitative studies 18 22.0

Mixed-method studies 9 11.0

Systematic reviews 12 14.6

Conceptual studies 6 7.3

Total 82 100.0

Table 6. Distribution of studies by publication year

Publication Year Number of Studies Percentage (%)

2020 8 9.8

2021 10 12.2

2022 14 17.1

2023 17 20.7

2024 18 22.0

2025 11 13.4

2026 4 4.8

Total 82 100.0

Publication output increased from 2020 to 2024, when the annual

count reached its highest level in the review corpus. The lower counts

in 2025 and 2026 should be interpreted in light of the search cutoff

rather than as evidence of declining research activity.

Table 7. Main thematic categories identified in the systematic review

Thematic Category Number of Studies Percentage (%)

AI-assisted recruitment and selection 21 25.6

Algorithmic performance evaluation 16 19.5

Employee well-being and mental health 15 18.3

Algorithmic fairness and transparency 13 15.9

Human resource analytics 9 11.0

Digital ethics and algorithmic governance 8 9.7

Total 82 100.0

Recruitment and selection represented the largest thematic

category, while digital ethics and algorithmic governance were less

frequently represented. This imbalance suggests that operational HR

applications have received greater attention than the governance

conditions required for responsible implementation.

Across the 82 studies, five recurring domains were identified:

AI-assisted recruitment and selection, algorithmic performance

evaluation, employee well-being and mental health, algorithmic

fairness and organizational trust, and ethical challenges in HRM.

Together, these domains capture both the organizational opportunities

and psychosocial risks associated with algorithmic HRM.

The reviewed evidence was concentrated mainly in technologically

advanced economies, while Latin American studies were

comparatively scarce. This geographical concentration limits

context-specific understanding of algorithmic HRM in emerging

economies and supports the need for broader cross-regional research

(Budhwar et al., 2022).

AI-Assisted Recruitment and Selection

AI-assisted recruitment and selection is one of the most extensively

studied applications. Organizations use algorithmic tools to screen

résumés, classify competencies, and support employment decisions,

but the benefits of speed and scale are accompanied by concerns

about data quality, discrimination, and accountability (Meijerink et

al., 2021; Raghavan et al., 2020).

The literature reports gains in processing capacity and consistency,

yet these advantages depend on the quality of data, model validation,

and the extent of human oversight. Algorithmic hiring therefore

requires governance mechanisms that combine technical performance

with procedural fairness.

A central risk is the reproduction of historical bias. Raghavan et al.

(2020) and Köchling and Wehner (2020) show that algorithmic

employment systems may create or perpetuate discriminatory

outcomes when training data, target variables, or validation

procedures reflect pre-existing inequalities.

Overall, technological efficiency does not automatically produce

fairer decisions. Without explainability, auditing, and human review,

complex models can make discriminatory or unjust outcomes more

difficult to detect and contest (Köchling & Wehner, 2020; Raghavan

et al., 2020).

Algorithmic Performance Evaluation and Employee

Experience

A second research stream concerns algorithmic performance

management. Algorithms can monitor output, set goals, schedule

work, generate ratings, and support performance-related decisions,

thereby reshaping organizational control and employee experience

(Kellogg et al., 2020; Parent-Rocheleau & Parker, 2022).

These systems can provide timely information and consistency, but

benefits depend on how employees experience the resulting work

design. Excessive monitoring or automated control may reduce

autonomy and increase perceived pressure.

Research on algorithmic management therefore identifies adverse

consequences when workers experience continuous surveillance,

restricted discretion, or opaque evaluation criteria (Kellogg et al.,

2020; Kinowska & Sienkiewicz, 2023).

From the JD–R perspective, algorithmic technologies can

simultaneously function as resources and demands. Their effects on

well-being depend on whether they improve access to useful

information and reduce burdens or instead intensify workload,

monitoring, and loss of control (Bakker & Demerouti, 2017;

Parent-Rocheleau & Parker, 2022).

Employee acceptance consequently appears closely linked to

transparency, participation, perceived fairness, and opportunities for

meaningful human influence over algorithm-supported decisions.

Employee Well-Being and Mental Health in Digital Work

Environments

The review also identified a consistent relationship between

algorithmic work systems and employee well-being. Digital

technologies can support well-being when they reduce repetitive

tasks, improve access to information, and enable more effective

development or workload decisions; they can undermine it when they

heighten insecurity, surveillance, or loss of autonomy (Kinowska &

Sienkiewicz, 2023; Parent-Rocheleau & Parker, 2022).

Recent HRM scholarship similarly emphasizes that algorithmic

technologies reshape work design and HR delivery, making human

agency and organizational context central to whether outcomes are

beneficial or harmful (Kim et al., 2025).

Negative outcomes include technostress, insecurity, emotional

strain, and reduced job satisfaction when workers perceive

automation as threatening or highly controlling. These risks reinforce

the importance of participatory implementation and safeguards for

autonomy and dignity.

J.Manage.Hum.Resour.(JulyDecember2026)4(2):2227

Table 5 shows that quantitative studies were the most common design

(45.1%), followed by qualitative studies (22.0%), systematic reviews

(14.6%), mixedmethod studies (11.0%), and conceptual studies (7.3%).

This distribution indicates a growing but methodologically diverse evidence

baseonalgorithmicHRMandemployeewellbeing.

Taken together, the evidence suggests that employee well-being is

not determined by AI alone but by the interaction between

technological design, work organization, leadership, governance, and

employee voice (Budhwar et al., 2022; Kinowska & Sienkiewicz,

2023).

Algorithmic Fairness, Transparency, and Organizational

Trust

Transparency and fairness emerged as recurrent governance

themes. Employees are more likely to accept algorithm-supported HR

decisions when criteria are understandable, contestable, and

accompanied by meaningful human oversight (Meijerink et al., 2021;

Parent-Rocheleau & Parker, 2022).

Opaque “black box” decision processes can increase perceptions of

organizational injustice because employees may be unable to

understand or challenge how employment-related recommendations

are produced. Research on algorithmic bias similarly stresses the need

for validation, auditing, and accountability (Kordzadeh &

Ghasemaghaei, 2022; Raghavan et al., 2020).

Organizational trust is therefore closely connected to the

governance of algorithmic systems. Clear communication, human

review, and opportunities to question decisions can help employees

perceive AI as a support mechanism rather than an uncontestable

form of control.

This interpretation is consistent with Kim et al. (2025), who

emphasize that the strategic value of algorithmic technologies

depends on how organizations integrate them with human agency and

broader organizational systems.

J.Manage.Hum.Resour.(JulyDecember2026)4(2):2227

ImplicationsforStrategicHumanResourceManagement

The findings show that AI is moving HRM toward more datadriven

and algorithmsupported models. This creates opportunities for faster

analysis and more consistent processes, but it also increases the

strategic importance of leadership, ethics, governance, and employee

participation.

Organizations that emphasize productivity (AspiazuSánchez &

EsquivelGarcía, 2025) without adequate transparency or human

oversight face greater risks of employee resistance, reduced autonomy,

and deteriorating wellbeing. Responsible implementation requires

explainability, fairness, data governance, and channels for employee

voice.

In this context, competencybased human resource management

remains particularly relevant, since the identification and development

of occupational competencies provide a foundation for training,

performance, and adaptation to changing organizational requirements

(MuñozSánchezetal.,2021).

Accordingly, the central managerial challenge is not simply

adopting advanced technologies but designing governance

arrangements that preserve employee rights, dignity, autonomy, and

trustwhilerealizingorganizationalbenefits.

Conclusions

This systematic review synthesized recent evidence on artificial

intelligence, algorithmic HRM, and employee wellbeing. The findings

show that algorithmic technologies are reshaping recruitment,

performance management, analytics, and work design while generating

bothorganizationalopportunitiesandpsychosocialrisks.

Employee wellbeing emerges as a multidimensional outcome

shaped not only by technological capability but also by transparency,

human oversight, fairness, work design, and employee participation. AI

can function as a resource when it reduces burdens and supports

decisions, but as a demand when it increases surveillance, insecurity, or

lossofautonomy.

The review contributes by integrating HRM, organizational

psychology, workdesign, and digitalgovernance perspectives. It also

highlights the limited geographical diversity of current evidence and

the need for more research in emerging economies, including Latin

America.

For managers, responsible algorithmic governance should guide

implementation. HR professionals should establish clear

accountability, explainability, human review, ethical data practices, and

digitalliteracyinitiativesthatsupportinformedemployeeparticipation.

For policymakers, the growing use of AI in recruitment, promotion,

performance evaluation, and career development reinforces the need

for safeguards against discrimination and for mechanisms that protect

fairness,transparency,andfundamentallaborrights.

This review has limitations. It was restricted to peerreviewed

studies retrieved from four major databases and to English or

Spanishlanguage publications. Exact platformspecific search syntax,

formal interrater reliability, and prospective protocol registration were

not documented in the source records. In addition, rapid technological

changemayquicklyaltertheevidencebase.

Future research should prioritize longitudinal, crosscultural, and

sectorspecific studies on wellbeing, mental health, trust, autonomy,

and job satisfaction, as well as stronger empirical evaluation of

algorithmicfairnessandgovernanceinemergingeconomies.

In conclusion, the longterm value of algorithmic HRM will depend

on organizations’ ability to combine technological innovation with

humancentered work design, ethical governance, and sustainable

employeewellbeing.

References

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de Ciencias Gerenciales, 7(12), 21–36.

https://doi.org/10.35381/gep.v7i12.200

Bakker, A. B., & Demerouti, E. (2017). Job demandsresources theory: Taking

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Conflicts of interest

The authors declare that they have no conflicts of interest.

Author contribution

Conceptualization: Barreto Júnior, J. M., Hong León, D. & Domingas

Chivango, N. Á. Data curation: Barreto Júnior, J. M., Hong León, D.,

Ramos Congo Mavambo, A., & Domingas Chivango, N. Á. Formal

analysis: Ramos Congo Mavambo, A., & Domingas Chivango, N. Á.

Research: Barreto Júnior, J. M., Hong León, D., Pérez Fernández, J. E.,

Ramos Congo Mavambo, A., & Domingas Chivango, N. Á.

Methodology: Barreto Júnior, J. M., Hong León, D., Pérez Fernández, J.

E. Supervision: Ramos Congo Mavambo, A., & Domingas Chivango, N.

Á. Validation: Barreto Júnior, J. M., Hong León, D., Pérez Fernández, J.

E., Ramos Congo Mavambo, A., & Domingas Chivango, N. Á.

Visualization: Barreto Júnior, J. M., Hong León, D., Domingas

Chivango, N. Á. Writing the original draft: Barreto Júnior, J. M., Hong

León, D., Pérez Fernández, J. E., Ramos Congo Mavambo, A., &

Domingas Chivango, N. Á. Writing, review, and editing: Barreto Júnior,

J. M., Hong León, D., Pérez Fernández, J. E., Ramos Congo Mavambo,

A., & Domingas Chivango, N. Á.

Data availability statement

The datasets used and/or analyzed during the current study, including

the data-extraction matrix identifying the 82 included studies, are

available from the corresponding author on reasonable request.

Statement on the use of AI

The authors acknowledge the use of generative AI and AI-assisted

technologies to improve the readability and clarity of the article.

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of Management and Human Resources.

Journal of Management and Human Resources and/or the editors

disclaim any responsibility for any injury to people or property resulting

from any ideas, methods, instructions, or products mentioned in the

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