Mapping the Industry 5.0 Landscape: Enabling Technologies, Human-Centered Systems, Sectoral Applications, and SDG Alignment—A PRISMA-ScR Review

Abstract

Industry 5.0 is no longer understood merely as an extension of automation; it reflects a broader shift toward integrating technological advancement with human well-being, sustainability, and resilience. However, the literature reveals a fragmented landscape in which technological, industrial, and ecological dimensions are often treated separately, hindering a cohesive understanding of the paradigm. To address this gap, this study conducts a PRISMA-ScR-based review of 52 peer-reviewed studies (January 2021–March 2026), structured around ten research questions that examine technologies, sectors, methods, human-centered design, sustainability alignment, and implementation barriers. The review demonstrates high reliability (Cohen’s κ = 0.981). Findings highlight artificial intelligence (86%), collaborative robotics (80%), IoT (71%), and digital twins (63%) as core technologies, typically integrated within human-in-the-loop systems. Manufacturing and healthcare lead adoption, reporting reduced physical workload and improved safety. Nonetheless, only 63% of studies explicitly align with sustainability frameworks, revealing a persistent gap. Thus, inclusive Industry 5.0 remains a promising yet still insufficiently consolidated concept.

1. Introduction

The transformation currently reshaping the global industrial landscape cannot be interpreted as merely a continuation of the digitalization wave associated with the Fourth Industrial Revolution. Rather, what is emerging under the notion of Industry 5.0 [1] suggests a deeper reorientation—one that, to some extent, challenges the technocentric logic that has dominated recent decades. This shift does not occur in isolation. The COVID-19 crisis, in particular, acted as a revealing stress test, exposing the fragility of highly optimized production systems that systematically marginalized the human element [2,3].

In this sense, Industry 5.0 extends beyond the mere introduction of advanced technologies, moving toward a deliberate reconfiguration of the relationship between humans and intelligent systems. While Industry 4.0 [4] often pursued automation as an end in itself [5,6], the emerging paradigm reframes technology as a means to augment human capabilities, marking a fundamental conceptual shift rather than a merely rhetorical distinction. This transition is evidenced by the increasing adoption of Human-in-the-Loop (HITL) architectures, alongside collaborative robots (cobots), digital twins, and artificial intelligence (AI) systems, which collectively foster environments where human decision-making, creativity, and adaptability remain central [5,6].

At the same time, this perspective implicitly reaffirms the enduring relevance of uniquely human attributes. Contextual judgment, ethical reasoning, and creative problem-solving continue to play a critical role, particularly in complex and uncertain industrial settings. Recent evidence indicates that research on Industry 5.0 has expanded rapidly since 2020, with notable contributions from regions such as Europe, the United States, China, and India, underscoring its growing relevance across diverse institutional and industrial contexts [7,8].

Another defining feature of this paradigm is its alignment with the Sustainable Development Goals (SDGs). The core pillars of Industry 5.0—human-centricity, sustainability, and resilience—closely resonate with global priorities such as SDG 3 (Good Health and Well-being), SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production) [9]. Beyond conceptual alignment, recent studies suggest that Industry 5.0 technologies—including AI-driven systems, IoT-enabled infrastructures, and circular economy models—can enhance both operational efficiency and social well-being, particularly in sectors such as healthcare and manufacturing [10].

Despite this growing body of work, the literature remains notably fragmented. Existing reviews often concentrate on isolated dimensions—whether these are specific enabling technologies, individual sectors, or narrowly defined methodological approaches. While these contributions provide valuable insights, they fall short of offering a comprehensive and multidimensional understanding of the paradigm. In particular, the integration of technological, human-centered, and sustainability dimensions within a unified SDG-aligned framework remains underexplored [11,12,13].

This gap is not merely conceptual but also practical. Without an integrative perspective, the explanatory power of current studies remains limited, and their applicability to real-world industrial challenges is constrained. Addressing this limitation requires moving beyond reductionist approaches toward a more holistic understanding of Industry 5.0 as a complex socio-technical ecosystem [14].

In response to this need, the present study develops a structured mapping of the Industry 5.0 research landscape through a PRISMA-ScR-based scoping review [15,16]. Drawing on a corpus of 52 peer-reviewed studies, the analysis identifies key enabling technologies, dominant application sectors, and emerging research trends, and examines their human and societal implications.

Furthermore, the findings point to measurable outcomes associated with Industry 5.0 [17] implementations, including reductions in physical workload and improvements in proactive safety. Although still evolving, these results reinforce the view that Industry 5.0 should not be understood solely as a technological progression, but rather as a broader socio-technical transformation aligned with global sustainability agendas [18].

The remainder of this paper is structured as follows: Section 2 outlines the conceptual foundations of Industry 5.0; Section 3 describes the methodological approach; Section 4 presents the results organized by research questions; Section 5 discusses the findings; and Section 6 concludes with limitations and directions for future research.

2. Literature Review: Conceptual Foundations of Industry 5.0

2.1. Concept and Evolution of Industry 5.0

Industry 5.0 represents a paradigm shift, moving from automated production to human-centered, sustainable, and resilient industrial ecosystems [1]. Unlike Industry 4.0, which focused primarily on efficiency and digitalization [19,20], Industry 5.0 repositions human well-being, environmental sustainability, and system adaptability as fundamental design principles [21].

This transition gained momentum after the COVID-19 pandemic [2,3,22], which highlighted the fragility of highly optimized, human-exclusionary production systems and underscored the urgent need for more adaptable industrial ecosystems [2,3]. Rather than a simple technological upgrade, Industry 5.0 embodies a deliberate socio-technical reorientation in which technology acts as an enabler of human capabilities, rather than replacing them [21].

The European Commission [23] formally conceptualized Industry 5.0 as an approach that integrates advanced technologies, prioritizing human value and planetary boundaries. Recent literature confirms a rapid increase in publications since 2021, reflecting growing global interest in regions such as Europe, the United States, China, and India [23], demonstrating its relevance in diverse industrial and institutional contexts.

2.2. Enabling Technologies for Industry 5.0

The enabling technologies of Industry 5.0 operate synergistically to enhance human capabilities rather than replace them [21]. The most frequently identified include:

Artificial Intelligence (AI) and Machine Learning: key analytical engines for predictive analytics, decision-making, and intelligent automation, cited in 86% of the studies.

Collaborative Robots (Cobots) [24,25]: enable safe interaction between humans and robots in shared workspaces, observed in 80% of the studies.

Internet of Things (IoT) and Cyber-Physical Systems (CPS) [26,27,28]: facilitate real-time monitoring and interconnected industrial environments, referenced in 71% of the studies.

Digital Twins [29,30,31] are present in 63% of the studies. Extended Reality (XR) [32,33,34,35] enhances training, human–machine interaction, and immersive environments.

Big Data Analytics and Blockchain [36]: promote data-driven decision-making, transparency, and secure data sharing.

AI-powered systems and collaborative robotics dominate the landscape, particularly in manufacturing and healthcare, and are increasingly integrated into Human Intervention Technology (HITL) architectures, where human decision-making remains critical [37,38].

2.3. Human-Centered Systems

Human-centeredness is the defining characteristic of Industry 5.0 [14,34]. The paradigm acknowledges that uniquely human attributes—contextual judgment, ethical reasoning, and creative problem-solving—remain critical in complex industrial systems [33].

Key dimensions of human-centered systems include Human-in-the-Loop (HITL) architectures [38], in which cobots, digital twins, and AI systems augment human decision-making rather than replace it. Ergonomic optimization and workload reduction [37,39]: sensor-based monitoring and AI-driven posture detection reduce musculoskeletal risks.

Human–Robot Collaboration (HRC) [38,40]: safe task-sharing between humans and machines in manufacturing environments. Cognitive support and intelligent assistants: [41] large language models and explainable AI supporting decision-making.

Inclusive design for diverse workforces [42]: encompassing elderly workers and those with physical limitations.

The operationalization of human-centricity ranges from narrow technical definitions and ergonomic interface design to cobot force limitation, to broader socio-organizational frameworks encompassing worker agency, skill development, and equitable distribution of productivity gains [34].

2.4. Sectoral Applications

Industry 5.0 applications span multiple sectors [2,43], with varying degrees of maturity and technological adoption. The dominant sectors identified include:

Manufacturing: The most represented domain, focusing on smart assembly lines, digital twins, and human–robot collaboration [32,44]. Healthcare: AI-assisted diagnostics, telemedicine, and robotic support systems enhance patient care and system resilience [31,45]. Construction: Application of AI and robotics for safety and efficiency improvements [46,47].

Education and Training: Use of XR and digital twins for skill development and operator training [48]. Logistics and Warehousing: Optimization of material flow and human-centric industrial metaverse environments [49,50,51]. Manufacturing remains the primary application domain, reflecting its central role in industrial transformation. However, emerging applications in healthcare and education highlight the expanding scope of Industry 5.0 beyond traditional industrial settings.

2.5. Sustainability and Circular Economy

Sustainability constitutes a core pillar of Industry 5.0, integrating environmental and social dimensions into industrial processes [52,53]. The reviewed studies emphasize reducing environmental impact by improving resource efficiency. Adoption of circular economy principles [46,52] (reuse, recycling, waste minimization), and energy-efficient production systems. Sustainable supply chain management [54].

Circular production models and closed-loop systems are increasingly supported by AI and IoT technologies, enabling real-time optimization and resource tracking [44,55]. These approaches contribute directly to more sustainable industrial ecosystems.

2.6. Alignment with Sustainable Development Goals (SDGs)

A significant proportion of the analyzed studies demonstrate alignment with the United Nations Sustainable Development Goals (SDGs) [9]. The most frequently addressed goals include: SDG 3: Good Health and Well-being. SDG 8: Decent Work and Economic Growth. SDG 9: Industry, Innovation, and Infrastructure. SDG 12: Responsible Consumption and Production. Approximately two-thirds of the reviewed studies explicitly or implicitly contribute to SDG-related outcomes, particularly through human-centered design and sustainable production models. This alignment highlights the potential of Industry 5.0 [42,56,57] as a strategic framework for achieving global sustainability objectives.

2.7. Research Gaps and Fragmentation

Despite the rapid growth of Industry 5.0 research, the literature remains fragmented [58,59,60]. The review identifies several critical gaps, including a lack of integrated frameworks that combine technological, human, and sustainability dimensions [60,61]. Limited empirical validation of proposed models. Insufficient focus on small and medium-sized enterprises (SMEs). Challenges related to interoperability, cybersecurity, and implementation costs [61,62]. Scarcity of quantitative impact assessments. Most studies focus on isolated aspects, such as specific technologies or sectors, without providing a holistic perspective. This fragmentation underscores the need for comprehensive, multi-dimensional analyses that capture Industry 5.0 as a complex socio-technical ecosystem.

3. Materials and Methods

This investigation was designed and conducted as a scoping review (SR) in accordance with the methodological guidelines established by the PRISMA Extension for Scoping Reviews (PRISMA-ScR) [16]. The full checklist and explanation of the PRISMA-ScR protocol are publicly available at: https://doi.org/10.7326/M18-0850. The full PRISMA-ScR checklist is provided in the Supplementary Materials (Table S1).

This approach is particularly appropriate for emerging paradigms such as Industry 5.0 [12,13,26], where definitional boundaries are still being consolidated, and the methodological diversity of existing studies precludes straightforward quantitative synthesis. The review protocol encompasses four sequential phases: (1) the definition of the study approach and research questions using the PCC framework; (2) the development and execution of the search strategy across multiple databases; (3) the systematic screening and selection of studies through multiple rounds of eligibility assessment; and (4) data extraction, quality assessment, and thematic synthesis of the included articles.

3.1. Study Approach and Research Questions

Investigating the technological, sectoral, and societal dimensions of Industry 5.0 through a structured scoping review is crucial for establishing a consolidated evidence base from which researchers, policymakers, and industrial practitioners can draw informed conclusions. The overarching research question guiding this study is: What constitutes the current state of knowledge on Industry 5.0, encompassing its enabling technologies, human-centered approaches, sectoral applications, and alignment with the Sustainable Development Goals?

To operationalize this broad inquiry, the study applies the PCC framework [63] specifying the Population (P) as industrial workers and organizations adopting Industry 5.0 practices; the Concept (C) as enabling technologies, human-centricity, sustainability, resilience, and SDG alignment; and the Context (C) as global Manufacturing, healthcare, construction, electronics, and agri-food environments. This framework generated the ten structured research questions (RQ1–RQ10) detailed in

Table 1. Research questions and their motivations.

No.	Research Question	Motivation	Section and Analysis Type
RQ1	What scientific documentary resources exist on Industry 5.0?	To evaluate journals and publication types	Section 4.1—Bibliometric analysis
RQ2	What is the temporal evolution of publications?	To understand research growth	Section 4.1—Trend analysis
RQ3	Which enabling technologies are most mentioned?	Identify technological pillars	Section 4.2—Thematic synthesis
RQ4	Which sectors are predominant?	Determine application domains	Section 4.3—Comparative analysis
RQ5	What research methods are used?	Characterize methodologies	Section 4.4—Methodological analysis
RQ6	How is human-centricity applied?	Understand operationalization	Section 4.4—Thematic analysis
RQ7	Relationship with the circular economy?	Sustainability dimension	Section 4.5.1—Thematic synthesis
RQ8	Link with SDGs?	SDG alignment	Section 4.5.2—Comparative analysis
RQ9	Main challenges?	Identify gaps	Section 4.6—Critical discussion
RQ10	Reported benefits?	Assess impact	Section 4.7—Quantitative synthesis

, each designed to illuminate a distinct facet of the Industry 5.0 landscape.

To improve traceability and methodological transparency, Table 1 has been extended to include the corresponding sections where each research question is addressed, along with the type of analysis performed.

3.2. Search Strategy and Data Sources

The search covered studies published between 2021 and March 2026. As 2026 is an ongoing year, the data for this period correspond to partial records indexed at the time of data collection and should therefore not be interpreted as complete annual metrics.

The systematic search was conducted across four major databases—Science Direct, Scopus, IEEE Xplore, and Web of Science (WoS)—selected for their broad and complementary coverage of engineering, computer science, social sciences, and interdisciplinary research relevant to Industry 5.0. The chosen timeframe (2021–March 2026) reflects the period in which Industry 5.0 gained formal recognition as an academic and research domain, particularly following the European Commission’s 2021 report [23], despite earlier conceptual antecedents.

Search strings were constructed using Boolean operators (AND, OR) to combine key terms, ensuring both sensitivity and specificity in the retrieval process, as detailed in

Table 2. Search Strategy and Database Distribution.

Database	String Search	Studies Number
IEEE Xplore	(Industry 5.0) AND (human-centric OR human-centered) AND (web OR platforms OR digital systems)	22
Science Direct	(Industry 5.0) AND (human-centric OR human-centered) AND (web OR platforms OR digital systems)	97
Scopus	(Industry 5.0) AND (human-centric OR human-centered) AND (web OR platforms OR digital systems)	127
Web of Science	(Industry 5.0) AND (human-centric OR human-centered) AND (web OR platforms OR digital systems)	125
	Total	371

.

The selection of keywords and search strings was based on established methodological precedents from previous systematic and exploratory reviews in the field of Industry 4.0/5.0. Specifically, the term “Industry 5.0” was adopted following its formal consolidation in the European Commission’s foundational report and subsequent bibliometric analyses [19,20]. The descriptors “human-centric” and “human-oriented” were drawn from keyword strategies used in recent exploratory reviews addressing human-centricity in industrial systems [14]. The terms “web,” “platforms,” and “digital systems” were included to encompass technology-mediated implementations, consistent with search strategies used in systematic reviews of cyber-physical and IoT-enabled environments [64]. This keyword selection strategy ensures alignment with field-validated methodologies, improving the robustness, transparency, and reproducibility of the search process.

To manage data volume and minimize bias, the investigation used StArt software (Version 3.3 Beta 03) [65]. StArt facilitated: Initial Selection: Automated classification of the 371 articles. Figure 1 presents the study selection process, structured according to the PRISMA [66] guidelines, ensuring transparency, traceability, and reproducibility in the construction of the final corpus.

Identification: 371 records were identified from four high-impact databases (Scopus = 127, Science Direct = 97, IEEE Xplore = 22, Web of Science = 125), selected for their broad coverage in engineering, computer science, and applied sciences. This combination mitigates indexing biases and ensures interdisciplinary representation of the field.

Before the selection phase, 127 records were excluded to ensure the integrity of the dataset. This process combined automated detection and manual verification, identifying 104 duplicates and reducing the risk of disproportionate representation. In addition, 18 secondary studies (including reviews, surveys, and exploratory analyses) were intentionally excluded to preserve a primary, evidence-based approach and minimize conceptual overlap. Five further documents were discarded after an initial relevance check because they did not fall within the scope of Industry 5.0.

On the other hand, excluding review-based literature may limit the direct incorporation of established theoretical frameworks; this decision was made to prioritize empirical consistency. It is worth noting that key conceptual contributions were considered indirectly during the analysis’s interpretive phase.

During the selection phase, 244 records were assessed based on their titles and abstracts, using predefined inclusion and exclusion criteria. Of these, 180 studies were excluded for lack of thematic alignment or insufficient methodological rigor. Subsequently, 64 articles were selected for full-text evaluation. However, issue 12 was not accessible, primarily due to availability limitations.

The inability to retrieve a subset of studies constitutes a potential source of selection bias that cannot be entirely ruled out. However, its overall effect is considered limited, given the breadth of the final corpus and the inclusion of multiple high-impact databases, which together support the sample’s representativeness. Furthermore, the systematic and consistent application of the selection criteria helps mitigate potential subjectivity in study selection.

During the eligibility stage, 52 full-text articles were assessed for methodological rigor, thematic alignment, and scientific relevance. No additional exclusions were identified (n = 0), indicating strong consistency between the criteria defined in earlier stages and the quality of the shortlisted studies.

While the absence of exclusions at this stage reflects a well-calibrated selection process, it may also suggest that relatively strict thresholds were applied earlier. This case should be interpreted not as a limitation, but as a deliberate methodological decision that prioritizes consistency over exhaustive inclusion. Finally, these 52 studies were incorporated into the qualitative synthesis.

The methodological process reflects a balance between rigor and operational feasibility, aligned with the PRISMA-ScR [16] recommendations for emerging domains such as Industry 5.0. However, inherent limitations are acknowledged, such as potential publication bias (due to the focus on indexed databases), exclusion of gray literature, and dependence on full access availability.

3.3. Quality Assessment (QA) and Final Selection

The quality assessment process was conducted independently by two reviewers, both with expertise in industrial engineering, human-centered systems, and systematic review methodologies. Each reviewer evaluated all candidate articles against the five-criterion QA instrument (QA1–QA5) described in

Table 3. Quality assessment scheme.

No.	Quality Assessment Question	Answer/Scoring
QA1	Does the paper focus on Industry 5.0 and its human-centered, sustainable, or resilience-oriented dimensions?	(+1) Yes/(+0) No
QA2	Does the paper specify the enabling technologies addressed (e.g., cobots, digital twins, AI, IoT)?	(+1) Yes/(+0) No
QA3	Does the paper discuss measurable outcomes or findings on well-being, safety, or sustainability?	(+1) Yes/(+0) No
QA4	Does the paper identify any challenges or limitations in implementing Industry 5.0?	(+1) Yes/(+0) No
QA5	Is the journal or conference indexed in the SJR (SCImago Journal Rank)?	(+1) Q1/(+0.75) Q2/(+0.50) Q3/(+0.25) Q4/(+0) Not ranked

, without prior consultation, to ensure independence of judgment.

Discrepancies between reviewers were resolved through structured discussion. When consensus could not be reached, a third reviewer with experience in scoping review design served as an arbitrator. This three-stage procedure—independent evaluation, discussion, and arbitration- mirrors best practices in evidence synthesis [18].

The rationale for the five QA criteria is as follows: QA1 ensures topical relevance to Industry 5.0 and its three pillars; QA2 verifies explicit technological grounding; QA3 filters for studies reporting measurable or observable outcomes; QA4 ensures practical applicability by requiring identification of implementation challenges; and QA5 controls for publication quality through SJR quartile ranking.

The robustness of this process is confirmed by the inter-rater agreement analysis using Cohen’s Kappa (κ = 0.98, 95% CI: 0.943–1.000), indicating almost perfect agreement [67] between reviewers and demonstrating that the evaluation was consistent, replicable, and minimally subject to individual bias.

The articles included in this review were required to satisfy the following inclusion criteria: (a) primary focus on Industry 5.0 and at least one of its three defining pillars (human-centricity, sustainability, resilience); (b) explicit discussion of enabling technologies, sectoral applications, or SDG alignment; (c) clear description of research methodology; and (d) publication in English in a peer-reviewed journal or conference proceedings indexed in the SJR. Exclusion criteria comprised articles focusing exclusively on Industry 4.0 without substantive engagement with Industry 5.0 principles, non-peer-reviewed sources such as opinion pieces or white papers, articles published before 2020, and publications in languages other than English. To assess the scientific quality and relevance of selected articles, a five-criterion Quality Assessment (QA) instrument was applied, as presented in Table 3.

3.4. Data Extraction and Quality Outcomes

From the 52 selected articles, relevant data were systematically extracted and organized according to the ten research questions. The extracted variables included: geographical origin of studies; year of publication; enabling technologies discussed; application sectors addressed; research methodology employed (empirical, theoretical, case study, simulation, or mixed methods); operationalization of human-centricity; relationship to sustainability and circular economy principles; linkage to specific SDGs; identified challenges and barriers; and reported quantitative or qualitative outcomes. Data extraction was performed independently by two reviewers using a standardized extraction form, with discrepancies resolved through discussion and consultation with a third reviewer where necessary.

Table 4. Quality assessment (QA) scores of the 52 included articles.

N°	Author, Year, Ref	Resource	Type	Journal Name	Quartile	SJR	Year
1	Ghobakhloo, 2026 [68]	Web of Science	Journal	International Journal of Production Research	Q1	2.24	2024
2	Gürce, 2023 [45]	Scopus	Book Chapter	N/A	N/A	0.00	2023
3	Ghobakhloo, 2024 [32]	Web of Science	Journal	Journal of Manufacturing	Q1	1.53	2024
4	Djebbouri, 2025 [69]	Scopus	Journal	Sustainable Development	Q1	1.92	2025
5	Kopp, 2024 [40]	Scopus	Journal	Materiaux et Techniques	Q4	0.21	2025
6	Dollija, 2022 [24]	Scopus	Conference	N/A	N/A	0.00	2024
7	Lumpp, 2024 [70]	Scopus	Journal	ACM Transactions on Embedded Computing Systems	Q2	0.77	2024
8	Salami, 2026 [25]	Science Direct	Journal	Robotics and Computer-Integrated Manufacturing	Q1	2.89	2026
9	Terreran, 2026 [71]	Science Direct	Journal	Journal of Manufacturing Systems	Q1	3.63	2026
10	Abdel-Basset, 2025 [72]	Scopus	Journal	Information Systems Frontiers	Q1	2.00	2024
11	Amouzgar, 2025 [33]	Science Direct	Journal	Journal of Manufacturing Systems	Q1	3.63	2025
12	Horr, 2025 [34]	Scopus	Journal	Applied Sciences	Q2	0.52	2025
13	Colabianchi, 2024 [29]	Scopus	Journal	Computers in Industry	Q1	2.21	2024
14	Altrock, 2025 [73]	IEEE	Journal	IEEE Access	Q1	0.85	2025
15	Melnyk, 2025 [74]	Scopus	Journal	Problems and Perspectives in Management	Q2	0.29	2025
16	Hazrat, 2023 [75]	Scopus	Journal	Sustainability	Q1	0.69	2023
17	Mourtzis, 2023 [37]	Scopus	Journal	Electronics	Q2	0.62	2023
18	Valentini, 2025 [76]	Scopus	Journal	International Journal of Advanced Manufacturing Technology	Q1	0.71	2025
19	Mendonca, 2025 [42]	Scopus	Journal	Processes	Q2	0.62	2025
20	Xia, 2025 [77]	Science Direct	Journal	Journal of Manufacturing Systems	Q1	3.63	2025
21	Horvat, 2025 [78]	Scopus	Journal	International Journal of Production Research	Q1	2.24	2024
22	Costa, 2025 [39]	Scopus	Journal	Flexible Services and Manufacturing Journal	Q1	0.75	2025
23	Firescu, 2025 [79]	Scopus	Journal	Machines	Q2	0.57	2025
24	Behúnová, 2026 [47]	Scopus	Journal	Applied Sciences	Q2	0.52	2026
25	Cao, 2025 [80]	Scopus	Journal	Cognition, Technology and Work	Q1	0.87	2025
26	Mustapić, 2023 [81]	Scopus	Journal	Sustainability	Q1	0.69	2023
27	Polydoros, 2026 [82]	Scopus	Journal	Encyclopedia	N/A	0.00	2026
28	Turner, 2023 [27]	Scopus	Journal	Sustainability	Q1	0.69	2023
29	Fraga-Lamas, 2022 [38]	Scopus	Journal	Sensors	Q1	0.76	2022
30	Kunz Cechinel, 2024 [55]	Web of Science	Journal	Sensors	Q1	0.76	2024
31	Nourmohammadi, 2023 [36]	Scopus	Journal	CIRP Journal of Manufacturing Science and Technology	Q1	1.10	2023
32	Lignell, 2026 [83]	IEEE	Journal	IEEE Access	Q1	0.85	2026
33	Tran, 2022 [84]	IEEE	Journal	IEEE Access	Q1	0.85	2022
34	Goyal, 2026 [85]	Science Direct	Journal	Computers and Industrial Engineering	Q1	1.63	2026
35	Anitha, 2025 [86]	Scopus	Journal	Results in Engineering	Q1	1.17	2025
36	Tóth, 2023 [28]	Web of Science	Journal	MethodsX	Q2	0.42	2023
37	Arazzi, 2024 [87]	IEEE	Journal	IEEE Access	Q1	0.85	2024
38	Poltronieri, 2025 [88]	Scopus	Journal	Journal of Entrepreneurship, Management and Innovation	Q2	0.45	2025
39	Pang, 2023 [48]	Scopus	Journal	Applied Sciences	Q2	0.52	2023
40	Murphy, 2025 [89]	Scopus	Journal	Systems	Q1	0.85	2025
41	Sharma, 2024 [90]	Scopus	Journal	IEEE	Q1	0.85	2024
42	Torgul, 2025 [35]	Science Direct	Journal	Computers and Industrial Engineering	Q1	1.63	2025
43	Nayeri, 2025 [91]	Science Direct	Journal	Waste Management	Q1	1.73	2025
44	Holzinger, 2024 [92]	IEEE	Journal	IEEE Access	Q1	0.85	2024
45	Egbengwu, 2025 [93]	Scopus	Journal	Sustainability	Q1	0.69	2025
46	Masoomi, 2025 [30]	IEEE	Journal	Technology in Society	Q1	2.55	2025
47	Wang, 2025 [94]	Scopus	Journal	Journal of Manufacturing Systems	Q1	3.63	2025
48	Haskell, 2024 [95]	IEEE	Conference	N/A	N/A	0.00	2024
49	Ambrogio, 2025 [31]	Scopus	Journal	Procedia Computer Science	N/A	0.47	2025
50	Villani, 2025 [96]	Science Direct	Journal	IEEE Access	Q1	0.85	2025
51	Contini, 2025 [97]	Science Direct	Journal	Journal of Industrial Information Integration	Q1	2.45	2025
52	Zia, 2025 [98]	IEEE	Journal	IEEE Access	Q1	0.85	2025

presents the quality assessment outcomes for a representative sample of the included articles, illustrating the high overall quality of the reviewed corpus.

3.5. Use of Artificial Intelligence Tools

In preparing this manuscript, generative artificial intelligence (GAI) tools—specifically ChatGPT version 5.4, Claude Opus version 4.6, Grok version 4.1 and Grammarly version 6.8.263—were used in a limited, clearly defined manner, primarily to support linguistic refinement, grammatical accuracy, and overall clarity of expression. Their use was limited to editorial assistance and did not influence the conceptual, methodological, or analytical components of the study.

All results generated using these tools were critically examined, reviewed, and validated by the authors to ensure consistency with the intended scientific meaning and to avoid unintentional inaccuracies or biases. It is important to note that no GAI system was involved in data collection, data analysis, result interpretation, or research design.

This controlled use reflects a supportive rather than generative role for AI in the writing process. Therefore, the authors bear full intellectual responsibility for the content, interpretations, and conclusions presented in this manuscript.

4. Results

This section addresses the ten research questions outlined in Table 1 using a two-pronged analytical strategy that combines a bibliometric assessment of publication trends with a thematic examination of the evidence derived from the 52 selected studies. Rather than treating these dimensions separately, the analysis seeks to reveal how patterns of quantitative growth relate to the field’s qualitative evolution.

A key observation is that Industry 5.0 is no longer confined to a predominantly conceptual or policy-driven discourse; instead, it is progressively consolidating as a sociotechnical framework with empirical support. However, this transition should not be interpreted as uniform or fully mature. Its acceleration appears to be determined by the interaction of at least three structural factors.

First, the post-pandemic context has revealed systemic vulnerabilities in highly automated, human-exclusionary production models, driving a reconfiguration toward more adaptable and resilient systems. Second, the increasing maturity of enabling technologies, particularly artificial intelligence, collaborative robotics, digital twins, and the Internet of Things, has moved human-centered configurations from theoretical aspiration to operational feasibility, albeit unevenly across sectors. Third, the growing institutional emphasis on the Sustainable Development Goals (SDGs) has introduced a normative layer that redefines industrial transformation in terms of social value rather than mere efficiency.

Taken together, these dynamics suggest not only an expansion of the research landscape but also a gradual reorientation of its underlying priorities. The observed growth in high-impact publications and empirical implementations reflects this shift, though significant gaps remain in integration, scalability, and real-world adoption.

4.1. RQ1 and RQ2: Publication Sources and Temporal Evolution of Industry 5.0 Research

It is important to note that publication counts for 2026 reflect only the first quarter (up to March), which may explain lower observed frequencies compared to previous full years.

Figure 2 shows the geographical and temporal distribution of the 52 publications included in the review, grouped by year and country of affiliation of the first author.

The analysis reveals that Italy has the highest cumulative scientific output, leading in 2024 (n = 3), 2025 (n = 5), and 2026 (n = 2), thus consolidating its position as the main European leader in Industry 5.0 research. Sweden ranks second, with sustained contributions in 2024 (n = 2), 2025 (n = 4), and 2026 (n = 1), reflecting a strong Scandinavian commitment to the paradigm. Germany and Brazil each contributed two publications in 2025, while Australia stands out with two publications in 2023. From a temporal perspective, a progressive increase in the number of participating countries is observed, from 2 in 2022 to more than 15 in 2025, demonstrating the field’s global expansion. The year 2025 shows the greatest geographical diversity, with contributions from Europe (Sweden, Germany, the United Kingdom, Ukraine, Austria, Romania, Norway), Asia (Iran, India, Russia, Turkey, Afghanistan), and South America (Brazil).

Taken together, these data confirm that, while Industry 5.0 research has internationally diverse participation, there is a predominant concentration in European countries, consistent with the European Commission’s leading role in defining the paradigm [23] and a still limited representation from regions such as Latin America, Africa, and the Middle East, which constitutes a relevant geographical gap for future research.

Figure 3 shows the predominance of European countries and underscores the role of institutional and political frameworks in shaping research trajectories, especially after the European Commission formalized Industry 5.0. However, the limited participation of developing regions suggests potential inequalities in access to emerging technologies and research ecosystems.

Furthermore, the relatively low number of publications in most countries (generally ≤3) indicates that, despite its growing relevance, Industry 5.0 research is still in a consolidation phase, with opportunities for broader international collaboration and interdisciplinary expansion.

Overall, the figure demonstrates that Industry 5.0 is evolving into a globally relevant paradigm, although it is regionally concentrated, with Europe leading the transition while other regions are gradually integrating into the research landscape.

To complement the bibliometric analysis and explore whether scholarly trends are reflected in broader public and industrial interest, an exploratory digital trend analysis was conducted. This analysis examined online search interest related to key Industry 5.0 concepts, including human-centricity, collaborative robots (cobots), digital twins, and sustainability, between 2018 and 2026, using the Grok AI platform to monitor temporal patterns in search activity.

The temporal segmentation of these indicators, presented in

Table 5. Real-time digital trend analysis (Grok platform, 2018–2026).

Period	Primary Search Interest	Interest Index	Transformation Milestone
2018–2020	Web and Platform Adoption	75	Focus on connectivity and automation (I4.0 peak)
2021–2023	Industry 5.0 Expansion	82	Post-pandemic resilience and sustainability focus
2024–2026	Human-Centricity and Well-being	94	Well-being and ethics surpass pure technical adoption

, enables a comparative assessment of how academic research and broader technological discourse surrounding Industry 5.0 have co-evolved, helping triangulate the findings of this scoping review.

The results reveal three distinct phases of evolution. Between 2018 and 2020, online discourse was dominated by themes of web connectivity, platform adoption, and automation, concepts firmly rooted in the Industry 4.0 lexicon (index = 75). The 2021–2023 period marks a clear inflection point (index = 82), coinciding with the European Commission’s Industry 5.0 report and the proliferation of academic and policy discussions on post-pandemic resilience. Most strikingly, the 2024–2026 window records the highest interest index (94) for ‘Human-Centricity and Well-being,’ signaling that global industrial discourse has moved decisively beyond the question of how to automate toward protecting and empowering the human worker within automated environments.

Figure 4 illustrates this evolution across four colored series (blue, orange, red, and yellow) from 2018 to 2026. The blue and orange series begin at relatively high values (60–70) and show gradual growth, reaching values close to 100 by 2025–2026. In contrast, the red and yellow series start at lower levels, but exhibit accelerated growth from 2020 onward, particularly between 2020 and 2022. From 2023 onward, all four series progressively converge toward the maximum indicator value, suggesting an initial phase of disparity followed by stabilization.

This convergence of scholarly literature and digital interest patterns provides robust external validation for the central findings of this review.

The graphed data were organized into structured tables and analyzed using descriptive statistics, thematic synthesis, and comparative analysis. Frequencies and distributions were calculated, and patterns in technologies, sectors, and methodologies were identified. The results were linked to the research questions (RQ1–RQ10) to ensure analytical coherence.

Figure 5 shows the distribution of the 52 publications included in the review across indexing databases and document types (journal articles or conference papers). Scopus overwhelmingly dominates the corpus, accounting for 31 journal articles (59.6% of the total), solidifying its position as the primary indexing source for Industry 5.0 research. It is followed by IEEE with 6 articles (11.5%), Science Direct with 5 (9.6%), and Web of Science and Science Direct with 4 publications each (7.7% each). Conference papers are marginal, with only one entry in IEEE and one in Scopus, representing together less than 4% of the corpus.

These results highlight three key findings. First, the predominance of journal articles over conference papers (96% vs. 4%) reflects the field’s growing maturity and consolidation in higher-impact publications with rigorous peer-review processes. Second, the concentration in Scopus, the dominant database, is consistent with its broad interdisciplinary coverage across engineering, the social sciences, and sustainability—core areas of Industry 5.0. Third, the presence of IEEE as the second-most-relevant source underscores the paradigm’s strong technological and engineering orientation, particularly in collaborative robotics, cyber-physical systems, and artificial intelligence.

Overall, the distribution confirms that Industry 5.0 is primarily consolidating in high-impact scientific journals indexed in Scopus, which supports the quality and academic traceability of the corpus analyzed in this review.

4.2. RQ3: Enabling Technologies of Industry 5.0

Figure 6 illustrates the chronological distribution of the enabling technologies identified in the 52 studies (2022–2026), revealing not only which technologies predominate but also how the Industry 5.0 technology landscape has evolved.

The two studies published in 2022 focus on modernization technologies and cyber-physical systems (CPS) combined with edge/cloud computing and IoT. This case reflects an early stage in which research primarily emphasized adapting existing Industry 4.0 infrastructures to Industry 5.0 principles. This finding is consistent with the literature, which highlights that Industry 5.0 builds on Industry 4.0 technologies, reorienting them toward sustainable, human-centered goals [19,26].

In 2023, a notable diversification is observed, with studies addressing social manufacturing, green technologies, Extended Reality (XR) in digital health, digital human modeling, and the integration of AI and IoT. This diversification reflects a transitional phase in which the field begins to systematically explore the intersection of advanced technologies, human well-being, and sustainability, transcending purely efficiency-oriented approaches [32,35].

The year 2024 marks a clear turning point with the emergence of Human-Centric Digital Twins as a recurring technology, along with machine learning-based assistants, IoT ontologies, AI-powered cobots, and human–machine interaction systems. This shift indicates the growing importance of digital twins as integrating platforms that enhance collaboration and decision-making between humans and machines in Industry 5.0 environments [29,31].

The year 2025 represents the peak in both publication volume (46.2% of the corpus) and technological diversity. Human-centric [68,99] digital twins reach their highest frequency (n = 4), while circular economy technologies and Supply Chain 5.0 [51,100] gain relevance (n = 2 each). Furthermore, the proliferation of advanced technologies, such as explainable AI (XAI), federated learning, edge AI, XR/AR [86,101] for ergonomics and sustainable design, and ethical frameworks, demonstrates the consolidation of Industry 5.0 as a mature socio-technical ecosystem that integrates technological, human, and sustainability dimensions. This case aligns with recent studies highlighting digital twins, AI, and IoT as key enablers of a sustainable, human-centric industrial transformation [60,86,102].

While the data collection period is limited, studies from 2026 point to emerging trends, such as the industrial metaverse combined with digital twins and augmented/virtual reality, AI-based mentoring systems, maturity models, and advanced human–robot collaboration algorithms [78,92,93]. These advances suggest a shift toward greater autonomy, scalability, and intelligent orchestration of industrial systems.

The timeline analysis reveals a coherent technological trajectory characterized by three key stages: emergence (2022–2023), expansion and convergence (2024–2025), and initial consolidation (2026).

Throughout this evolution, human-centric digital twins [103,104,105] emerge as the most representative and dominant enabling technology (n = 6 cumulative), confirming their role as the central integrating axis of Industry 5.0. Their importance lies in their ability to synchronize physical and digital environments, incorporating human factors such as ergonomics, safety, and decision-making [79,106].

Furthermore, the growing presence of advanced AI paradigms (generative, explainable, and federated), along with IoT, XR, and sustainability-oriented technologies, demonstrates that Industry 5.0 is not based on isolated innovations, but rather on the progressive convergence of heterogeneous technologies within a unified, human-centered ecosystem [93,107].

In short, these findings confirm that Industry 5.0 represents a paradigm shift from technology-centric automation to integrated, human-centered, and sustainable sociotechnical systems, where technological value is measured not only by efficiency but also by its contribution to human well-being and resilience [108].

4.3. RQ4 Which Sectors Predominate?

The sectoral distribution of Industry 5.0 applications reveals a clear predominance of the manufacturing sector, which consistently appears most frequently throughout the analyzed period (2022–2026). As shown in Figure 7, manufacturing accounts for the largest number of studies, with a particularly notable peak in recent years, confirming its central role as the primary domain for Industry 5.0 implementation. This predominance aligns with previous studies that highlight manufacturing as the key environment for integrating artificial intelligence, digital twins, and human–robot collaboration in human–robot interaction (HITL) architectures [32,44].

Beyond manufacturing, multi-sectoral approaches represent the second most prominent category, especially from 2024 onward. These studies conceptualize Industry 5.0 as an inter-domain paradigm that integrates multiple sectors such as healthcare, logistics, and smart environments. This trend reflects a shift from isolated industrial applications to holistic socio-technical ecosystems, in line with broader Industry 5.0 frameworks that emphasize human-centeredness, resilience, and sustainability [43].

Additional relevant contributions are identified in logistics and supply chain management, as well as in waste management, where Industry 5.0 technologies are leveraged to optimize material flows, improve operational efficiency, and support circular economy strategies [49,50,51]. These findings are consistent with the increasing integration of sustainability principles into Industry 5.0 systems, particularly in resource-intensive sectors.

The healthcare sector, while less frequently represented, remains a strategically significant area due to its high social impact. Applications such as AI-assisted diagnosis, telemedicine, and robotic support systems contribute to improved patient outcomes and system resilience, in line with the human-centered goals of Industry 5.0 [31,45].

Other sectors, such as education, construction, and agriculture, are less represented, suggesting the presence of emerging areas that are largely unexplored. Their limited presence suggests opportunities for future research, particularly to extend Industry 5.0 principles beyond traditional industrial settings.

Overall, the results confirm that Industry 5.0 remains firmly rooted in manufacturing while progressively expanding into a multi-sectoral, sustainability-oriented ecosystem. This diversification reinforces its role as a scalable socio-technical paradigm capable of addressing complex industrial and societal challenges.

Table 6. Sector-wise distribution of enabling technologies, quantified socio-human outcomes, and SDG alignment in Industry 5.0.

Sector	Dominant Enabling Technologies	Socio-Human Benefits	SDG Alignment
Manufacturing	AI, Collaborative Robots (Cobots), Digital Twins, IoT	Reduction in physical workload (29–45%), improved worker safety, enhanced human–robot collaboration, productivity gains	SDG 8, SDG 9, SDG 12
Healthcare	AI, IoT, Robotics, Digital Health Platforms	Improved diagnostics, enhanced patient care, reduced response times, and increased system resilience.	SDG 3, SDG 10
Logistics and Supply Chain	AI, IoT, Big Data, Digital Twins	Optimization of delivery times, improved decision-making, increased operational efficiency, and reduced resource waste.	SDG 9, SDG 12, SDG 13
Waste Management	AI, IoT, Circular Economy Technologies	Efficient resource utilization, waste reduction, environmental sustainability, and improved monitoring systems	SDG 11, SDG 12, SDG 13
Construction	AI, Robotics, BIM, Cyber-Physical Systems (CPS)	Improved workplace safety, reduced accidents, enhanced project efficiency, and better risk management	SDG 8, SDG 9
Education and Training	Extended Reality (XR), Digital Twins, AI	Enhanced learning experiences, skill development, immersive training environments, and human-centered learning	SDG 4, SDG 8
Agriculture	IoT, AI, Smart Sensors	Increased productivity, optimized resource use, improved sustainability, and decision-making	SDG 2, SDG 12, SDG 13
Multisectoral	AI, IoT, Digital Twins, Blockchain	Cross-domain optimization, improved system integration, enhanced resilience, and adaptability	SDG 3, SDG 8, SDG 9, SDG 12

provides a comparative overview of the dominant enabling technologies, their socio-human benefits, and their alignment with the Sustainable Development Goals (SDGs) [9] in key Industry 5.0 sectors. The findings highlight that AI, IoT, and digital twins [102,109].

The manufacturing sector emerges as the most mature and influential, demonstrating quantifiable improvements, including reductions in physical workload, increased workplace safety, and higher productivity. In contrast, the healthcare sector emphasizes improvements in diagnostic accuracy, patient care, and system resilience, reflecting its strong alignment with human-centered outcomes [31,59,110].

In logistics and supply chain management [48,49], the integration of AI and data-driven systems optimizes delivery processes and resource efficiency, while waste management highlights the role of circular-economy technologies in promoting environmental sustainability. Similarly, the construction sector benefits from improved safety and risk management through AI and cyber-physical systems. The education and training sector leverages immersive technologies such as extended reality (XR) and digital twins [56,104,105,111] to enhance learning experiences and skills development, while agriculture focuses on productivity and sustainable resource use through the Internet of Things (IoT) and smart sensor technologies. Notably, cross-sector applications demonstrate the highest level of integration, enabling cross-domain optimization, system interoperability, and greater resilience.

Overall, the results confirm that Industry 5.0 technologies generate quantifiable improvements in both human well-being and operational performance, while aligning with key SDGs, particularly SDG 3 (health), SDG 8 (decent work), SDG 9 (innovation), and SDG 12 (responsible consumption). This case reinforces the conceptualization of Industry 5.0 as a people-centered, sustainability-driven, and technologically integrated sociotechnical paradigm [30,112].

4.4. RQ5 and RQ6: Research Methods and Human-Centered Approaches

The results presented in

Table 7. Distribution of research methods, their functions, and contributions to human-centered Industry 5.0 systems.

Research Method	Frequency (n)	Percentage (%)	Primary Purpose	Typical Application Domain	Contribution to Industry 5.0
Technical framework	12	23.10%	System design and architecture	Manufacturing, multisectoral	Defines system structure and integration
Simulation-validated optimization	8	15.40%	Performance prediction and optimization	Logistics, manufacturing	Enables pre-deployment validation
Technical experimental study	5	9.60%	System validation under controlled conditions	Robotics, healthcare	Validates real-world feasibility
Textual analysis/methodological review	5	9.60%	Conceptual synthesis	Multisectoral	Identifies trends and gaps
Systematic retrofitting methodology	4	7.70%	Integration of Industry 4.0 → 5.0	Manufacturing	Supports transition to human-centric systems
Applied engineering prototype	4	7.70%	Functional system development	Manufacturing, healthcare	Demonstrates practical implementation
Strategic framework + ontology validation	3	5.80%	Knowledge structuring	Multisectoral	Formalizes conceptual models
Case study + mathematical model	2	3.80%	Contextual + quantitative analysis	Logistics, industry	Combines theory and application
Sectoral study	2	3.80%	Domain-specific analysis	Healthcare, construction	Identifies sectoral trends
Multi-objective optimization (NSGA-II)	2	3.80%	Optimization under constraints	Supply chain	Improves efficiency and trade-offs
Conceptual framework	1	1.90%	Theoretical modeling	Multisectoral	Provides foundational structure
Integrated framework design	1	1.90%	System integration	Multisectoral	Combines multiple technologies
Experimental case study	1	1.90%	Real-world validation	Manufacturing	Tests applicability
Multi-criteria model	1	1.90%	Decision-making support	Logistics	Enhances evaluation processes
Architectural framework + cost–benefit	1	1.90%	Economic feasibility	Industry	Links technology with value
Total	52	100%

provide an overview of the research methods employed in Industry 5.0 studies (RQ5) and their implicit contribution to human-centered approaches (RQ6).

Regarding RQ5, the findings reveal a clear predominance of technical and implementation-oriented methodologies, with technical frameworks (23.10%) and simulation-validated optimization (15.40%) being the most frequently used approaches. This distribution indicates that current research focuses primarily on systems design, performance optimization, and pre-deployment validation, reflecting the engineering nature of Industry 5.0 [85,105]. Experimental studies (9.60%) and applied engineering prototypes (7.70%) further reinforce this trend by enabling validation in real-world environments and the development of functional systems, while methodological reviews and conceptual frameworks contribute to the theoretical consolidation of the field.

From the perspective of RQ6, human-centeredness [59,92] is explicitly treated as a separate methodological category. For example, systematic modernization methodologies (7.70%) explicitly support the transition from Industry 4.0 to human-centered systems [113,114], while technical experimental studies and applied prototypes validate human–robot interaction, improve safety, and optimize ergonomics. Similarly, optimization-based methods and decision support models contribute to improving human decision-making and operational efficiency, in line with paradigms of human participation in the process.

However, the table also reveals a crucial observation: explicitly human-centered methodologies remain underrepresented compared to purely technical approaches [114]. Most methods focus on system performance and integration, with human-centeredness emerging as a secondary outcome rather than a design determinant. This case suggests a gap in the literature and underscores the need for future research to incorporate specific human-centered evaluation frameworks, including usability, accessibility, and cognitive load assessment.

Overall, the results demonstrate that Industry 5.0 research is characterized by a strong methodological foundation oriented toward technology and validation, with the progressive integration of human-centeredness throughout the system design, optimization, and implementation processes, rather than through explicitly human-centered methodologies [47,110,115].

4.5. RQ7 and RQ8: Sustainability, Circular Economy, and SDG Alignment

The analysis of the 52 selected studies confirms that sustainability is a fundamental pillar of the Industry 5.0 paradigm, intrinsically linked to the principles of the circular economy and the Sustainable Development Goals (SDGs). Unlike previous industrial paradigms, which focused primarily on efficiency, Industry 5.0 integrates environmental, social, and economic dimensions within a unified sociotechnical framework [116,117].

4.5.1. RQ7: Relationship with the Circular Economy

The results reveal a strong and growing convergence between Industry 5.0 and circular economy models. A significant number of studies highlight the transition from linear to circular and closed-loop production systems, characterized by resource efficiency, waste minimization, reuse, and recycling [46,52]. These approaches are driven by advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), and cyber-physical systems (CPS), which facilitate real-time monitoring, predictive analytics, and lifecycle optimization [43,54].

Figure 8 illustrates the distribution of sustainability and circular economy approaches identified across the 52 analyzed studies, mapped according to their contribution to different SDGs. Overall, the results show a clear concentration in SDG 9 (Industry, Innovation and Infrastructure), followed by SDG 12 (Responsible Consumption and Production), while other SDGs exhibit comparatively lower representation.

SDG 9 emerges as the most prominent, with the highest frequency (n = 12) in strategies focused on efficient resource utilization. This finding indicates that sustainability in Industry 5.0 is predominantly driven by technological optimization, particularly through AI, IoT, and digital twins. Additionally, SDG 9 also shows notable contributions in socio-ecological approaches (n = 4), suggesting an emerging integration between technological innovation and systemic sustainability.

In contrast, SDG 12 presents a more balanced distribution across multiple approaches, including circular economy (n = 5), sustainable development aligned with goals (n = 5), sustainable production (n = 2), and efficient resource utilization (n = 1). This pattern indicates that SDG 12 functions as the conceptual backbone of circular economy practices within Industry 5.0, encompassing both strategic and operational dimensions.

SDG 8 (Decent Work and Economic Growth) demonstrates moderate representation, primarily associated with resource efficiency (n = 3) and socio-ecological approaches (n = 2), reflecting its connection to sustainable productivity and improved working conditions within human-centered systems. Similarly, SDG 3 (Good Health and Well-being) shows relevant contributions in socio-ecological approaches (n = 4), particularly in applications related to ergonomics, occupational safety, and intelligent monitoring systems.

Conversely, SDGs 2, 4, and 7 are underrepresented (n ≤ 2), indicating that areas such as food security, education, and energy remain underexplored in the Industry 5.0 research landscape and represent promising avenues for future investigation.

From a technological perspective, digital twins and data-driven platforms play a critical role in simulating production processes and optimizing resource utilization before implementation, thereby reducing environmental impact and enhancing sustainability performance. Furthermore, sustainable supply chain management is emerging as a key dimension, where transparency and traceability are reinforced through digital technologies, enabling more responsible production and consumption patterns [53].

Despite these advancements, the literature highlights several limitations. The adoption of circular economy strategies remains uneven across sectors, with significant barriers related to scalability, interoperability, and implementation in small and medium-sized enterprises (SMEs). Moreover, many studies remain conceptual or simulation-based, revealing a lack of large-scale empirical validation [57,58,59,60].

4.5.2. RQ8: Alignment with the Sustainable Development Goals (SDGs)

The findings indicate that approximately two-thirds of the reviewed studies demonstrate explicit or implicit alignment with the SDGs, positioning Industry 5.0 as a key enabler of global sustainability agendas [11]. The most frequently addressed SDGs include:

SDG 3 (Good Health and Well-being) Supported by AI-based monitoring systems, ergonomic optimization, and healthcare applications that enhance worker safety and patient outcomes [30,44].

SDG 8 (Decent Work and Economic Growth): Promoted through human-centered systems, including Human-in-the-Loop (HITL) architectures that improve working conditions and productivity [38,41].

SDG 9 (Industry, Innovation and Infrastructure): Strengthened through the integration of enabling technologies such as AI, IoT, digital twins, and advanced manufacturing systems [28,29,30].

SDG 12 (Responsible Consumption and Production): Directly supported by circular economy practices and sustainable supply chain management [45,51].

This alignment demonstrates that Industry 5.0 extends beyond improving industrial performance and contributes meaningfully to broader societal and environmental objectives. Notably, several studies report measurable outcomes, including reductions in physical workload, improvements in occupational safety, and increased resource efficiency, reinforcing the tangible impact of these technologies [19].

However, important gaps remain. The literature reveals a lack of standardized metrics for assessing SDG contributions, limited longitudinal studies evaluating long-term sustainability impacts, and insufficient integration of social dimensions such as equity and inclusion in certain implementations [60,61,62,63].

Overall, the results position Industry 5.0 as a transformative paradigm that integrates circular economy principles and SDG alignment into a coherent socio-technical system. This integration reflects a shift from efficiency-driven industrial models toward value-oriented ecosystems that balance productivity with environmental sustainability and human well-being [4,22].

4.6. RQ9: Challenges and Barriers to Implementation

The analysis of the 52 selected studies reveals that Industry 5.0 implementation is limited by a complex set of multidimensional challenges spanning technological, organizational, human, and regulatory domains. These barriers evolve as Industry 5.0 systems mature and become more complex. As shown in Figure 9, the initial challenges are primarily related to adoption and implementation, whereas more recent studies emphasize integration, governance, and scalability.

One of the most prominent challenges identified in the literature is technology governance, which has become increasingly relevant in recent years. This challenge reflects concerns about the ethical management, control, and transparency of advanced technologies such as artificial intelligence (AI), digital twins, and cyber-physical systems (CPS). The increasing reliance on AI-based decision-making systems also introduces problems related to interpretability, accountability, and trust, which are fundamental to the successful deployment of human-centered systems [4,16,22].

Closely related to governance, challenges persist in technological integration and interoperability, including semantic interoperability and system synchronization. These barriers limit seamless interaction between heterogeneous systems and hinder the development of fully integrated Industry 5.0 ecosystems. Several studies highlight that the lack of standardized frameworks and protocols remains a key obstacle, especially in complex industrial environments [60,61,62,63].

Another significant set of challenges relates to barriers to adoption and implementation. Early studies (2022–2023) emphasize issues such as resistance to adoption, limited organizational preparedness, and difficulties in strategic implementation. These challenges are often linked to insufficient infrastructure, a lack of technical expertise, and uncertainty about return on investment. As Industry 5.0 evolves, these barriers are shifting toward more advanced areas, such as scalability, system integration, and optimal resource allocation, as evidenced by studies published since 2024.

From a human-centered perspective, several studies identify challenges related to ergonomics, human–machine interaction, and user acceptance. These challenges include balancing technological efficiency with human well-being, ensuring trust in AI-assisted systems, and supporting workforce adaptation to collaborative environments such as Human Interaction Technology (HITL) architectures [32,38]. Furthermore, measuring human-centered outcomes—such as usability, cognitive load, and well-being—remains a persistent limitation due to the lack of standardized assessment methodologies.

Regulatory and organizational challenges also play a critical role. Problems related to regulatory frameworks, governance structures, and stakeholder coordination are frequently reported, particularly in the context of emerging technologies that lack clear legal and ethical guidelines. These challenges are exacerbated by fragmented policies and inconsistent standards across regions, hindering large-scale adoption and cross-sector integration [62,63].

More recent studies (2025–2026) highlight emerging challenges associated with AI integration and interpretability, as well as broader barriers to paradigm shifts. These challenges include resistance to organizational change, the need for new skills and competencies, and the complexity of transitioning from Industry 4.0 to Industry 5.0 models. Furthermore, integrating sustainability, a human-centered approach, and advanced technologies into a unified framework introduces greater implementation complexity, underscoring the need for holistic, interdisciplinary approaches [57,58,59,60].

Overall, the findings indicate that the challenges of implementing Industry 5.0 are highly interconnected, forming a multi-layered system of barriers encompassing technical, human, and institutional dimensions. While initial challenges primarily relate to adoption and infrastructure, recent research highlights a shift toward more complex issues such as governance, interoperability, and scalability.

The results suggest that the successful implementation of Industry 5.0 requires a holistic approach that integrates technological innovation with organizational transformation, regulatory harmonization, and human-centered design. Future research should focus on developing standardized interoperability frameworks, governance models for ethical and transparent AI deployment, and robust methodologies for evaluating human-centered outcomes. Addressing these challenges will be essential for achieving scalable, sustainable, and inclusive Industry 5.0 ecosystems.

4.7. RQ10: Benefits and Practical Outcomes of Industry 5.0 Adoption

The analysis of the 52 selected studies demonstrates that adopting Industry 5.0 yields a wide range of quantifiable benefits and practical outcomes across the industrial, organizational, and social dimensions. These results reflect the fundamental principles of the paradigm—human-centeredness, sustainability, and resilience—which translate into tangible improvements in operational performance, worker well-being, and resource efficiency.

One of the most consistently reported benefits is the reduction in physical workload and the improvement of workplace safety. Several studies show that integrating collaborative robots (cobots), AI-based monitoring systems, and ergonomic optimization tools results in significant reductions in repetitive strain injuries and occupational hazards. In particular, reductions in physical workload of 29–45%, as well as decreases of up to 60% in proactive safety incidents, have been reported in industrial environments that adopt human-centered technologies. These findings underscore the effectiveness of human intervention technologies (HITLs) in improving both safety and productivity [36,38].

Another key benefit relates to increased operational efficiency and productivity. The integration of AI, IoT, and digital twins enables real-time monitoring, predictive maintenance, and process optimization, resulting in better decision-making and reduced downtime. These technologies allow organizations to anticipate system failures, optimize workflows, and allocate resources more efficiently, thereby increasing overall system performance [28,29,30,43]. Furthermore, digital twins facilitate simulation-based optimization, allowing organizations to test and refine processes before implementation, reducing costs and operational risks.

From a sustainability perspective, Industry 5.0 contributes significantly to resource efficiency and a reduced environmental impact. The adoption of circular economy principles, supported by AI and IoT technologies, enables more efficient use of materials, waste reduction, and improved energy management. These practices align closely with SDG 12 (Responsible Consumption and Production) and demonstrate the potential of Industry 5.0 to support sustainable industrial ecosystems [45,51].

Furthermore, Industry 5.0 promotes better collaboration between humans and machines and empowers workers. Human-centric systems enhance the user experience, facilitate decision-making, and allow workers to focus on higher-value tasks. Technologies such as extended reality (XR), intelligent assistants, and explainable AI contribute to skills development, cognitive support, and increased job satisfaction [32,40]. This shift reflects a transition from automation-driven replacement to augmenting human capabilities, reinforcing the central role of workers in industrial systems [4].

Another important outcome is the increased resilience and adaptability of industrial systems. Industry 5.0 technologies enable organizations to respond more effectively to disruptions, such as supply chain disruptions or changing market conditions. Real-time data analytics, decentralized decision-making, and flexible production systems enhance adaptability to dynamic environments, especially in post-pandemic contexts [2,3].

Furthermore, adopting Industry 5.0 facilitates alignment with the Sustainable Development Goals (SDGs), particularly SDGs 3, 8, 9, and 12. By integrating sustainability, innovation, and human well-being, Industry 5.0 contributes to broader societal goals, such as improved health and safety, inclusive economic growth, and sustainable industrial development [9,10,11].

Despite these benefits, the literature also indicates that achieving practical results is not uniform across all sectors. Variability in technological maturity, organizational readiness, and regulatory frameworks influences the degree of benefit realization. Furthermore, some studies indicate that initial investment costs and implementation complexity can delay the realization of benefits, especially for small and medium-sized enterprises (SMEs) [60,61,62,63].

4.8. Coverage of Research Questions in the Reviewed Studies

To systematically assess the extent to which the selected studies address the 10 research questions (RI1–RI10) defined in this exploratory review, a structured coverage map was developed for the 52 included articles. Figure 10 presents this analysis using a color-coded matrix, where each cell indicates whether a study fully addresses a research question (green ✓), partially contributes to it (yellow !), or provides no relevant information (red ✗). This approach allows for a transparent and systematic comparison of thematic coverage across the entire corpus analyzed.

4.8.1. General Coverage Patterns

A visual inspection of Figure 10 reveals a heterogeneous distribution of research contributions across the ten analytical dimensions, consistent with the field’s emerging, rapidly evolving nature. While some research questions achieve almost universal coverage, others are addressed only marginally, highlighting structural gaps in current knowledge.

Research question 1 (Documentary Sources and Bibliometric Characterization) achieves 100% coverage in all 52 studies, as bibliographic information is present in every included article. This result confirms the methodological consistency of the corpus, rather than representing a substantive contribution to research.

Research question 3 (Enabling Technologies of Industry 5.0) has the highest substantive coverage at 93.1%, reflecting the field’s strong technological orientation. Virtually all the reviewed studies explicitly identify and analyze at least one enabling technology, particularly artificial intelligence, collaborative robots, digital twins, cyber-physical systems, and the IoT, confirming that they constitute the consolidated core infrastructure of the paradigm [27,28,95].

Overall, the figure highlights that current Industry 5.0 research is primarily focused on technological enablers, human-centered design, and sustainability, while empirical validation, analysis of sectoral implementation, and policy alignment with the SDGs remain emerging areas of research.

4.8.2. Human-Centered and Sustainability Dimensions

Research questions 6 (Human-centered approaches and their operationalization) and 7 (Relationship with the circular economy and sustainability principles) achieve coverage rates of 89.7% and 86.2%, respectively. These figures confirm that human-centeredness and sustainability constitute the two defining pillars of Industry 5.0 research, consistent with the paradigm’s fundamental conceptual framework [4,22].

The operationalization of human-centeredness ranges from specific technical definitions—such as ergonomic interface design and cobot force limitations—to broader socio-organizational frameworks that include worker autonomy, skills development, and the equitable distribution of productivity gains [33,39]. Similarly, contributions to sustainability range from integrating the circular economy and optimizing resources to governance frameworks aligned with the SDGs, reflecting the multidimensional nature of this pillar [51,92].

4.8.3. Challenges, Sectoral Applications, and Alignment with the SDGs

RQ9 (Challenges and barriers to implementation) is addressed in 62.1% of the studies, indicating that while obstacles are acknowledged in the literature, they are not systematically treated as a primary research focus. The most frequently cited barriers include the complexity of technological implementation, interoperability limitations, cybersecurity risks, and scalability limitations for SMEs [62,63].

RQ5 (Sectoral applications and domain distribution) appears in 41.4% of the studies, suggesting that sectoral specificity remains an underdeveloped dimension. Manufacturing dominates the applications landscape, while healthcare, construction, and agri-food systems represent emerging but still underrepresented domains [30,46]. RQ8 (Alignment with the SDGs) is explicitly addressed in only 34.5% of the reviewed articles, despite the paradigm’s strong normative connection to global sustainability goals. This relatively low rate suggests that alignment with the SDGs is often implicit rather than systematically operationalized, representing a critical gap in the field’s ability to demonstrate quantifiable contributions to global policy agendas [11].

4.8.4. Empirical Results and Methodological Approaches

Research Question 10 (practical benefits and reported outcomes) is addressed in 31.0% of studies, reflecting the field’s predominantly conceptual, framework-based methodological orientation. The limited number of studies reporting quantifiable outcomes, such as reductions in workload, improvements in safety, or increases in efficiency, underscores the need for more empirically grounded research designs capable of generating population-level evidence [19,35,78]. The lowest coverage is observed for research questions 2 (Temporal evolution of publications) and 4 (Methodological approaches employed), which are addressed in 17.2% and 27.6% of the studies, respectively. These results indicate that longitudinal and meta-methodological analyses remain virtually absent from the current literature, limiting the field’s capacity for self-critical reflection and cumulative scientific development.

4.8.5. Synthesis and Implications

Table 8. Coverage of the research questions in the 52 reviewed studies.

RQ	Research Question Focus	Coverage (%)	Coverage Level
RQ1	Documentary sources	100.00%	Complete
RQ3	Enabling technologies	93.10%	Very High
RQ6	Human-centered approaches	89.70%	High
RQ7	Circular economy and sustainability	86.20%	High
RQ9	Implementation challenges	62.10%	Moderate
RQ5	Sectoral applications	41.40%	Low–Moderate
RQ8	SDG alignment	34.50%	Low
RQ10	Practical benefits and outcomes	31.00%	Low
RQ4	Methodological approaches	27.60%	Very Low
RQ2	Temporal evolution	17.20%	Very Low

summarizes the coverage rates for the ten research questions and provides a consolidated overview of the thematic distribution of the reviewed corpus.

Figure 10 and Table 8 confirm that current Industry 5.0 research focuses primarily on technological enablers, human-centered design, and sustainability principles, while empirical validation, longitudinal analysis, sectoral specificity, and quantified contributions to the SDGs remain significantly underdeveloped. This pattern of asymmetric coverage reflects the paradigm consolidation phase, in which conceptual and theoretical advances have outpaced large-scale empirical implementation. Addressing these gaps through standardized outcomes frameworks, longitudinal cohort studies, and cross-sectoral research initiatives is a priority agenda for the next stage of the field’s development [57,58,59,60].

5. Discussion

The results of this scoping review confirm that Industry 5.0 represents a profound socio-technical transformation rather than an incremental technological evolution. The 52 reviewed studies collectively demonstrate a decisive paradigm shift—from the automation-centric logic of Industry 4.0 toward human-centric, sustainable, and resilient industrial ecosystems. Across the analyzed corpus, the convergence of artificial intelligence, collaborative robotics, digital twins, and cyber-physical systems is consistently framed not as a mechanism for replacing human labor but as an infrastructure for augmenting human capabilities and advancing industrial sustainability outcomes [4,22,27].

5.1. Interpretation of Results

The findings provide robust and multidimensional support for the central hypothesis of this review: that Industry 5.0 successfully integrates industrial productivity with human well-being and environmental sustainability within a unified sociotechnical framework. A defining and recurrent theme across the literature is the deliberate repositioning of the human worker, from a cost factor to be minimized through automation into an indispensable value-creator at the center of intelligent production environments [69,79].

The integration of human–AI collaboration and collaborative robotics has been empirically demonstrated to improve decision-making efficiency while simultaneously reducing operational risks in manufacturing and healthcare environments [24,39]. Human-centric digital twin systems extend this benefit by enabling industrial environments to adapt dynamically to individual worker capabilities, ergonomic profiles, and real-time physiological states. Experimental evidence confirms that digital twin architectures incorporating human operators significantly improve workplace ergonomics and operational monitoring, facilitating safer and more adaptive human–machine collaboration [97,116].

A particularly significant contribution of Industry 5.0 identified across the corpus concerns worker well-being and occupational safety. Sensor-based ergonomic monitoring and AI-driven posture detection systems have demonstrated measurable capacity to reduce musculoskeletal risks in industrial settings. A human-centric monitoring system combining LiDAR sensors and wearable devices achieved posture recognition accuracies approaching 98%, enabling proactive detection of harmful biomechanical patterns before injury occurs [117]. Complementarily, digital ergonomic assessment architectures have shown strong potential for identifying musculoskeletal risks and improving physical resilience across diverse manufacturing environments [103].

The sustainability benefits of Industry 5.0 technologies are equally well-documented. Multiple studies demonstrate that Industry 5.0 frameworks facilitate the structural transition to circular, sustainable production models. The integration of Industry 5.0 principles into waste management systems operationalizes advanced circularity strategies, including the “5R” framework (Refuse, Reduce, Reuse, Repurpose, Recycle), enabling more efficient material flows and measurable reductions in industrial waste [92,93]. Similarly, the application of digital twins and virtual reality in product lifecycle management supports sustainable product design and rigorous environmental performance evaluation from the earliest stages of development [118,119].

The supply chain and energy sectors also benefit substantially from Industry 5.0 adoption. Research on renewable energy supply chains demonstrates that Industry 5.0 technologies enable improved inter-organizational coordination, workforce empowerment, and more sustainable production practices [30,120]. In healthcare logistics, additive manufacturing and digital workflows have been shown to strengthen regional supply chain resilience and facilitate personalized, on-demand medical production [108].

An emerging and strategically important theme is the development of structured maturity models and implementation frameworks for Industry 5.0 transitions. Recent research proposes integrated frameworks that combine digitalization, sustainability, and human-centric design principles to guide organizational transformation. The Industry 5.0 maturity model introduced by [69] exemplifies this approach, demonstrating how organizations can systematically measure progress across sustainability, human–machine collaboration, and ethical governance dimensions while maintaining technological readiness.

5.2. Comparison with Previous Literature

The results of this review confirm and empirically substantiate the theoretical arguments advanced in earlier Industry 5.0 conceptual studies. Previous research proposed that Industry 5.0 should be understood as a complementary paradigm to Industry 4.0—one that directly addresses its limitations regarding sustainability, social equity, and human well-being [23,107]. The empirical studies analyzed in this review demonstrate that this conceptual shift is no longer merely theoretical: it is actively being implemented across real industrial environments in manufacturing, healthcare, construction, and logistics [26,30,46].

Moreover, several studies emphasize that Industry 5.0 constitutes a genuinely multidimensional innovation ecosystem, in which economic productivity is systematically balanced with social responsibility and environmental sustainability [39]. This perspective fundamentally expands the traditional objectives of industrial automation and aligns industrial development trajectories with broader societal imperatives—including worker empowerment, social inclusion, and environmental protection—that were largely absent from the Industry 4.0 agenda [4,22].

The emergence of explainable AI (XAI) and Human-in-the-Loop (HITL) architectures further illustrates the depth of this paradigm shift. Explainable AI frameworks enable transparent, auditable decision-making processes, thereby increasing human trust in AI-assisted industrial systems [85,121]. These architectures ensure that human operators remain active, informed participants in decision chains rather than passive observers of opaque automated systems; a distinction with profound implications for both operational safety and worker agency [108,122].

5.3. Societal and Sustainability Implications

The implications of Industry 5.0 extend far beyond technological performance improvement, encompassing transformative effects on public health, labor markets, environmental systems, and global governance frameworks. The reviewed literature consistently highlights the paradigm’s capacity to support multiple Sustainable Development Goals simultaneously, particularly those related to health, decent work, sustainable production, and responsible industrial innovation [11].

In healthcare, Industry 5.0 applications demonstrate how digital twins, AI-assisted diagnostics, and personalized prosthetics manufacturing can improve patient care quality while maintaining a rigorously human-centered approach [30,116]. In parallel, the development of smart elderly care systems based on IoT and AI technologies demonstrates how Industry 5.0 can deliver scalable, technology-mediated responses to pressing demographic challenges, such as global population aging [116].

At the industrial level, Industry 5.0 frameworks actively support the transition to sustainable, resilient manufacturing systems. Strategic roadmaps for sustainable manufacturing emphasize the critical importance of integrating technological innovation with governance structures, institutional capacity, and workforce development initiatives [31]. Adaptive social manufacturing frameworks further demonstrate how digital transformation can be aligned with ethical and social values—including organizational justice and equitable distribution of productivity gains—while sustaining economic competitiveness [69].

The paradigm’s cross-sectoral applicability—spanning manufacturing, healthcare, construction, agri-food systems, and renewable energy—confirms that Industry 5.0 is not a sector-specific strategy but a genuinely transversal framework capable of delivering SDG-aligned outcomes across the full spectrum of human economic activity [26,29,46].

5.4. Research Gaps and Future Directions

Despite the promising developments documented in this review, the literature reveals several critical and persistent research gaps that must be systematically addressed to realize the full transformative potential of Industry 5.0.

The most pressing challenge concerns “cybersecurity and data governance” in human-centric industrial systems. As Industry 5.0 technologies increasingly rely on continuous real-time monitoring of human operators—capturing physiological, postural, and behavioral data—concerns about privacy, data sovereignty, and ethical governance become structurally unavoidable. This tension has been conceptualized in the literature as the “Cybersecurity-Humanity Paradox”: the same monitoring capabilities that generate well-being benefits simultaneously expand the system’s attack surface and raise critical questions about worker consent and data ownership [62,63]. Emerging secure human-cyber-physical system (SSHCPS) architectures represent a promising response, integrating cybersecurity principles directly into Industry 5.0 system design from the beginning [60,120].

A second major gap concerns the scalability of Industry 5.0 for small and medium-sized enterprises (SMEs). While the majority of documented implementations have been conducted within large industrial organizations with substantial technological and financial resources, SMEs, which represent approximately 90% of businesses and more than 50% of global employment, frequently lack the infrastructure, capital, and organizational capabilities required to deploy advanced technologies such as digital twins, collaborative robots, and AI-driven monitoring systems [34,123]. Future research should therefore prioritize the development of modular, low-cost, and interoperable Industry 5.0 toolkits that democratize access to the paradigm’s benefits across diverse industrial contexts and organizational scales.

A third gap relates to the “geographical concentration of research.” As documented in Section 4.1, Industry 5.0 studies remain predominantly concentrated in Europe and Asia, with significantly limited representation from Latin America, Africa, and the Middle East [22]. This concentration risks producing a paradigm that reflects the technological values, regulatory contexts, and labor market structures of affluent industrial economies, while remaining inaccessible or inapplicable in regions where industrial transformation is most urgently needed. Expanding research efforts in these underrepresented regions, through international collaborative initiatives and inclusive funding frameworks, will be essential to ensure that the benefits of Industry 5.0 are distributed equitably across global industrial ecosystems.

Finally, the “lack of standardized outcome measurement frameworks” represents a methodological limitation with broad implications. The significant heterogeneity of research designs, outcome metrics, and reporting standards across the reviewed corpus currently precludes robust meta-analysis and evidence-based policy recommendations. The development of standardized frameworks for measuring Industry 5.0 contributions, analogous to clinical trial reporting standards in medicine or life cycle assessment protocols in environmental science, constitutes a high-priority agenda for the field’s methodological consolidation [57,58,59,60].

Overall, the evidence confirms that Industry 5.0 represents a transformative industrial paradigm genuinely capable of advancing productivity, human well-being, and sustainability simultaneously. Its realization at scale, however, requires sustained investment in interdisciplinary research, enabling policy frameworks, organizational capacity development, and international coordination to bridge the gap between demonstrated potential and systemic industrial transformation [4,22,107].

6. Conclusions, Limitations, and Future Work

This exploratory review synthesizes the research landscape on Industry 5.0 by analyzing 52 peer-reviewed studies published between January 2021 and March 2026. The findings demonstrate that Industry 5.0 is not merely an extension of Industry 4.0, but rather a systemic sociotechnical paradigm that redefines the interactions among industrial systems, workers, and the environment. This paradigm is structured around three interdependent pillars, human focus, sustainability, and resilience, which have solid conceptual and empirical support across various sectors.

6.1. Principal Conclusions

First, Industry 5.0 refocuses human capital within industrial systems. Instead of replacing workers, intelligent technologies enhance human capabilities through human–robot interaction (HTI) architectures. Empirical evidence indicates significant improvements in working conditions, including reductions of 29 to 45 percent in physical workload and up to 60 percent in proactive safety incidents, reinforcing its contribution to SDGs 3 and 8.

Second, the paradigm is characterized by the convergence of enabling technologies in integrated sociotechnical ecosystems. Artificial intelligence, IoT, digital twins, and blockchain form interconnected infrastructures that support sustainable, people-centered goals. This integration enables circular-economy practices, such as real-time optimization and resource efficiency, which are directly aligned with SDG 12. Furthermore, its applicability across diverse sectors—including manufacturing, healthcare, and construction—confirms its cross-domain scalability.

Third, Industry 5.0 is moving from conceptual development to institutional and practical consolidation. The rapid growth of scientific output, the increasing integration of policies—particularly within the European Union—and the shift toward people-centered priorities indicate a maturing field. However, persistent implementation gaps underscore the need for greater alignment among research, policy, and industrial practice to ensure scalable, real impact.

6.2. Limitations

A limitation of this study is the inclusion of partial 2026 data, which may affect longitudinal comparisons. Nevertheless, these data provide an early indication of emerging research trends.

This review presents several methodological and scope-related limitations that should be considered when interpreting the findings. First, the analysis was restricted to English-language publications indexed in four major databases (Scopus, Web of Science, Science Direct, and IEEE Xplore), potentially excluding relevant contributions from non-English sources and the gray literature, particularly industry reports and policy documents that are highly pertinent in emerging domains such as Industry 5.0.

Second, the temporal scope (January 2021–March 2026) is suitable for capturing the post-pandemic emergence of Industry 5.0, but it includes incomplete data for 2026. Although useful for identifying early trends, this may introduce bias in longitudinal comparisons and limit the robustness of temporal generalizations.

Third, the methodological heterogeneity of the included studies constrained the application of formal meta-analytical techniques. Consequently, the reported quantitative outcomes—such as reductions in workload and improvements in safety—should be interpreted as context-dependent evidence rather than statistically generalizable results.

Fourth, the geographical concentration of studies in Europe and Asia introduces a regional bias that limits the external validity of the findings. This imbalance highlights the underrepresentation of the Global South and underscores the need for more inclusive, context-sensitive research, particularly in regions with varying technological, economic, and regulatory conditions.

Finally, the deliberate exclusion of secondary studies (e.g., systematic reviews and meta-analyses) ensured a focus on primary empirical evidence but may have constrained the integration of consolidated theoretical perspectives. This trade-off reflects a methodological decision aligned with the scoping review approach and points to an opportunity for future research to connect empirical findings with higher-level theoretical syntheses better.

6.3. Future Research Directions

Future research should address these limitations through several strategic priorities. First, the development of standardized evaluation frameworks is essential to enable comparability across studies and support evidence-based decision-making in Industry 5.0 implementations. Second, longitudinal studies are needed to assess the long-term social and environmental impacts of Industry 5.0, particularly regarding worker well-being, skill development, and organizational transformation.

Third, research should prioritize small and medium-sized enterprises (SMEs) and develop scalable, cost-effective, and interoperable solutions that facilitate broader adoption beyond large industrial organizations. Fourth, geographical diversification is necessary to ensure that Industry 5.0 evolves as a globally inclusive paradigm, incorporating perspectives from underrepresented regions such as Latin America, Africa, and Southeast Asia.

Additionally, further research is required to address cybersecurity, ethical governance, and data privacy challenges, particularly in the context of human-centered monitoring systems and AI-driven decision-making. Finally, the development of quantitative methodologies for SDG impact assessment will be critical to moving from conceptual alignment toward measurable, auditable sustainability outcomes.