From Industry 4.0 to 5.0: leveraging AI and IoT for sustainable and human-centric operations Open Access

Industry 5.0 redefines manufacturing by synergizing human intellect with artificial intelligence (AI) and robotics, fostering collaboration over competition. This paradigm promises not only to enhance productivity and drive innovation but also to stimulate economic growth while creating more jobs than it displaces (Nahavandi, 2019). The industrial evolution pivots towards the emergence of Operator 5.0, enabled by advanced technologies such as Digital Twins and Mixed Reality. Integration of prototype technologies like Bluetooth, RFID, and mobile applications now automates office facilities, including lighting, temperature control, and security, allowing users to remotely control environments and improve accessibility and comfort. These innovations leverage advancements in 5G and the Internet of Things (IoT) to deliver cost-effective, user-friendly solutions that boost productivity and overall quality of life (Mourtzis et al., 2022).

Unlike Industry 4.0, which emphasized automation, Industry 5.0 prioritizes human well-being and sustainable practices by fostering collaboration and aligning innovation with humanitarian goals and the United Nations Sustainable Development Goals (SDGs). It addresses the complex ramifications of digital transformation on labor markets, such as job displacement, wage disparities, and automation, through multifaceted theoretical perspectives that stress the importance of human-centric technology, adaptive skillsets, and robust policy frameworks to guide the future of work (Kolade and Owoseni, 2022).

In the maritime sector, digital transformation efforts aim to improve sustainability and efficiency; however, the human element in autonomous operations remains underrepresented. Concepts like Marine 5.0 and Seafarer 5.0 have been introduced to emphasize the crucial role of human intellect in maritime evolution (Shahbakhsh et al., 2022). The COVID-19 pandemic further underscored the necessity for AI-driven modernization to strengthen supply chains, especially in emerging economies. Industry 5.0’s nine AI imperatives, supported by real-time IoT tracking, are critical for enhancing post-pandemic supply chain resilience (Ahmed et al., 2023).

Technological advancements in AI, virtual reality (VR), and integrated manufacturing are transforming industries and giving rise to “smart” working environments. Contemporary assessments of Industry 5.0 critically examine mass customization approaches and explore socio-technical design methods for these intelligent systems (Bednar and Welch, 2020). This study adopts a dual theoretical lens. First, Socio-Technical Systems Theory (STS), which views technological adoption as a co-evolving relationship between humans and machines, supports the study’s emphasis on collaborative design. Second, Human-Centered Design serves as a value-based framework that aligns innovation with the ethical, emotional, and practical needs of human users, core to the ideals of Industry 5.0. Industry 5.0 is also characterized as the “Age of Augmentation,” emphasizing cyborg mutualism and addressing ethical challenges posed by technology’s societal impact. Value Sensitive Design (VSD) frameworks have been advocated to integrate human values into future manufacturing technologies (Longo et al., 2020).

Practical advancements include novel mixed-integer linear programming (MILP) models and adaptive simulated annealing techniques to optimize automobile assembly lines (Nourmohammadi et al., 2022). Meanwhile, Green IoT (G-IoT) paradigms combined with Edge AI have been proposed to reduce energy consumption and carbon footprints in IoT systems (Fraga-Lamas et al., 2021). Real-world Industry 5.0 use cases also highlight the role of mist computing and AI-enabled IoT nodes in promoting sustainability. Additionally, metaverse technology is emerging as a transformative digital force, raising the need to responsibly manage its rapid adoption. Environmental, social, and governance (ESG)-based frameworks aligned with SDGs have played a pivotal role in guiding the transition to Industry 5.0 by balancing technological innovation with sustainability (Rosen et al., 2023).

While the transition from Industry 4.0–5.0 has attracted increasing scholarly attention, much of the existing literature remains concentrated on technological advancements such as collaborative robotics, predictive maintenance, and cyber-physical systems. However, a critical gap persists in understanding how these innovations translate into social sustainability, ethical adoption, and inclusive design, particularly within post-pandemic industrial ecosystems. This study identifies that although various frameworks and case studies highlight the potential of AI and IoT in enhancing productivity, they often lack a human-centered theoretical grounding and empirical focus on value-sensitive design and societal well-being. Through a scientometric synthesis, the paper provides a consolidated view of current research trajectories and explicitly uncovers underexplored domains such as workforce inclusion, digital ethics, and sustainable customization. This mini-literature review thus reveals a structural gap in aligning technological deployment with ethical and socio-technical objectives a void this study aims to fill.

In light of these developments, this study seeks to address the following research questions:

1. What are the key themes, trends, and influential works in the literature on Industry 5.0?

2. How do emerging technologies like AI, ML, and IoT contribute to sustainable and human-centric industrial transformation?

3. What are the current challenges and future directions for Industry 5.0 research, based on scientometric insights?

This study makes several novel contributions to both theory and practice. Theoretically, it advances the discourse on Industry 5.0 by framing it as a socio-technical paradigm shift that demands integration of human-centered design principles and ethical innovation frameworks. It introduces a conceptual synthesis of how technologies such as AI, ML, and IoT are evolving from operational tools into enablers of inclusive, sustainable, and resilient systems. In terms of practical implications, the paper offers valuable scientometric insights that can guide SMEs, digital supply chains, and healthcare sectors in designing future-ready, human-centric operations. By mapping existing trends and surfacing unaddressed areas such as Green IoT, ethical AI, and value-sensitive applications, this study supports both policymakers and industrial practitioners in navigating the humanization of smart technologies.

The rest of the paper is organized as follows: Section 2 describes the research methodology, including data collection and analysis techniques. Section 3 presents the results and insights into emerging trends in Industry 5.0. Section 4 discusses the findings in detail and incorporates both theoretical and managerial implications. Finally, Section 5 concludes the study by summarizing key contributions, discussing limitations, and offering future research recommendations.

A most popular and exacting technique for looking through and evaluating vast amounts of scientific data is bibliometric analysis. It allows us to examine the subtleties of a particular topic’s evolution while illuminating new directions in that field (Donthu et al., 2021). When bibliometric analysis is compared to other well-known methods, such as meta-analysis and systematic literature review, the two approaches essentially evaluate the existing literature using a systematic procedure and assess the strength and direction of the relationship in the literature, respectively (Aguinis et al., 2011).

This study employs two prominent bibliometric techniques to analyze the evolution and structure of the literature: bibliographic coupling and co-word analysis. The analysis covers research published between 2010 and 2024, providing a comprehensive overview of developments and emerging trends within this timeframe.

The study adopts a structured approach to data analysis involving document and keyword evaluation. As illustrated in Figure 1, the process begins with database selection, keyword searching based on Industry 5.0, and applying inclusion criteria to filter relevant journal articles. The data is then analyzed using VOSviewer through two paths bibliographic coupling for document analysis and co-word analysis for keyword evaluation, leading to cluster map generation, top-ranking tables, and thematic development based on data relevance.

1. Bibliographic coupling:

The process of identifying conceptual parallels while referencing a document is known as bibliometric coupling. Recent research publications, which have fewer citations, are also taken into account (Pandey et al., 2024). Bibliographic coupling relations were also visualized at the document/articles level to give a visual depiction of the most cited choice modeling papers and their subject commonalities (Haghani et al., 2021).

1. Co-word analysis:

Co-word analysis is utilized to understand how much each research topic has developed and to identify possible avenues for future study (Tan Luc et al., 2022). By identifying frequently occurring keywords and their correlations, co-word analysis provides insights into the dynamic growth of subjects and captures the intellectual structure of research fields (Kulakli and Arikan, 2023). By showing changes in scholarly focus across decades and detecting clusters of similar terms, co-word analysis plays a crucial role in forecasting new research subjects (Yan and Zhiping, 2023).

The threshold of 13 minimum co-word occurrences and 41 citations for bibliographic coupling was determined through initial exploratory analysis of frequency distributions. Threshold values were selected at inflection points where meaningful topic clusters began to form while minimizing noise and overlap. This approach ensured both analytical focus and visualization clarity. Data were extracted from the Scopus database in April 2024, covering a publication window from January 2010 to March 2024. This date range captures the full span of relevant literature from the early emergence of Industry 5.0 research to the most recent developments.

Thematic clustering was conducted using a two-stage procedure with the support of VOSviewer software (Donthu et al., 2021). Initially, VOSviewer generated clusters automatically based on network visualization techniques applied to bibliographic coupling and co-word analysis. These clusters were formed by identifying patterns of citation or keyword co-occurrence, grouping documents or terms that demonstrated strong link strengths and conceptual proximity (Donthu et al., 2021; Tan Luc et al., 2022). Following this automated step, a manual thematic interpretation was applied to label each cluster. This involved reviewing the key publications, keywords, and conceptual content within each cluster to identify prevailing topics and research orientations. Based on this qualitative assessment, descriptive labels were assigned to each cluster to best represent their thematic focus (van Eck and Waltman, 2010).

We employed the following search string (Table 1) to identify publications based on relevant keywords. The search query relates to Industry 5.0, and industrial revolutions are determined based on the literature, synonyms, and thesaurus. The search was performed on the Scopus database using the topic search option. Publications that contain keywords in the title, abstract, and authors’ names are covered by this option. Additionally, only journal papers are included in this analysis; conference proceedings, books, book chapters, and editorials are not. The quality of peer-reviewed papers included in the science mapping analysis is ensured by restricting the analysis to journal publications alone (Fauzi et al., 2024).

The search creates an adaptive human-centered production system by using Industry 5.0 and ML alongside AI and IoT. ML algorithms analyze vast IoT data in real-time, enabling predictive maintenance and efficient resource allocation. This supports sustainable development by minimizing waste and reducing energy use. This innovation in ML-driven automation advances eco-friendly practices within Industry 5.0, aligning industrial growth with environmental and social goals.

Figure 2 is drawn from a CSV file of 558 documents, which shows the number of papers on the x-axis and years on the y-axis. And bars exhibit the number of publications per year. Publications are growing at an exponential rate, especially after 2021. An important peak is reached in 2024 when there are about 300 publications. Very few papers were published in earlier years, indicating either the emergence of a new research area or increased interest/resources in recent times.

This graph compares publication growth for both paradigms shown in Figure 3. Industry 4.0 dominated scholarly output until 2020. However, from 2021 onward, Industry 5.0 research has grown rapidly, suggesting a shift toward more human-centric and sustainable technological narratives.

Out of the 558 documents, 59 met a threshold of 41 citations in the bibliographic coupling. As a result, these 59 documents create 8 clusters. Until the network visualization produced the most reliable and suitable number of clusters for additional interpretation, the threshold was established through a series of experiments. Before the most stable map was created, the threshold was tested several times. A threshold that is too high could result in over-filtering, while a threshold that is too low could result in under-filtering. To guarantee that the scientific map generated could accurately depict the underlying literature of Industry 5.0 inside the ecosystem of SMEs, the threshold was carefully chosen. The value of interest is the publication’s total like strength (TLS), as bibliographic coupling considers the link of the citing publication. The top-3 documents based on total link strength (TLS) are Leng et al. (2022) (56 TLS), Javaid and Haleem (2020) (53 TLS), and (Javaid et al., 2020) (66 TLS). See Table 2.

Figure 4 presents the network visualization of bibliographic coupling. The three clusters are visibly independent of one another. The following discusses current trends and future IR 5.0, AI, and ML development. The clusters are labeled based on inductive interpretation by revisiting representative articles in the clusters and synthesized based on common themes and research streams presented.

1. Cluster 1 (red): Fundamentals of Industry 5.0

With its emphasis on resilience, sustainability, and human well-being, Industry 5.0 signifies a revolutionary change in global manufacturing. It seeks to promote wealth and sustainable development for everybody, going beyond conventional industrial objectives like growth and employment. Industry 5.0 is still in its infancy and has not yet been thoroughly studied, but it is based on three fundamental ideas: resilience, sustainability, and human-centricity (Leng et al., 2022). AI, IoT, big data, cloud computing, blockchain, digital twins, edge computing, collaborative robots, and 6G are some of the key technologies driving Industry 5.0. In addition to increasing productivity, these technologies relieve people of tedious, filthy, and repetitive duties.

Global industry transformation will result from intelligent systems’ increased control over production and supply chains (Humayun, 2021). Industry 5.0 and Society 5.0 tackle economic, environmental, and social issues by addressing the manufacturing value chain beyond CPS. Based on the socio-cyber-physical system (SCPS), Industry 5.0 is envisioned as a socio-technical revolution that integrates cutting-edge technology to establish a manufacturing paradigm that is more robust, sustainable, and human-centric (Yao et al., 2024). The advanced technology of Industry 5.0 proffers the manufacturing sectors fresh viewpoints to develop resilient, human-centered, and sustainable methods. Supply chains play a crucial role in carrying out these goals by linking suppliers and users and offering value-added goods and services. Nevertheless, the manufacturing sector’s deliberation of this paradigm shift is still vague despite expanded interest (Dacre et al., 2024).

1. Cluster 2 (green): Transition to Industry 5.0 and human-centric approach

Industry 5.0 introduced novel AI-based techniques that proficiently map employer expressions associated with well-being using job advertisements. The process involves creating a large vocabulary of well-being terms, which are then compared to the corpus of recent research. This approach can more smoothly analyze employers’ perspectives on empirical well-being. By bridging the theoretical and practical realms, we give academics and business people helpful knowledge about how employers perceive well-being (human-centricity). Grybauskas et al. (2022) finding presents how UK companies value self-discovery and a healthy work environment to attract recruits. However, many job advertisements do not specifically emphasize well-being to engage potential applicants.

Industry 4.0 transforms manufacturing and the value chain by integrating cutting-edge technologies like blockchain, IoT, and AI. It emphasizes overcoming technical obstacles, governance, and digital integration. The next stage of industrial evolution, known as Industry 5.0, is also examined (Lu, 2021). The exploration of the transformation of Industry 4.0 to the new paradigm of Industry 5.0 is referred to as the Age of Augmentation and includes flawless synergy between human and machine collaboration. The transformation highlights the significance of value-oriented and ethically developed advanced technologies as a pillar of Industry 5.0, drawing on perspectives from noteworthy figures in the field. It encourages the Value Sensitive Design (VSD) framework, a systematic approach to integrating human values into technology systems’ architecture (Longo et al., 2020).

The next stage of the industrial revolution, known as Industry 5.0, is centered on sustainability and human-centered advancements. By using technologies like blockchain, IoT, and AI to encourage human capacities and evolve more robust and moral systems, it enlarges Industry 4.0. With the perfection and speed of machines combined with human innovations and problem-solving capabilities, Industry 5.0 seeks to build a more buoyant, sustainable, and just future (van Erp et al., 2024). Real-time smart healthcare capabilities made possible by Industry 5.0 technology were essential during the COVID-19 epidemic. They facilitate individualized treatment, remote monitoring, and allow physicians to concentrate on their most important patients. By enhancing healthcare delivery and results, these technologies also support medical education for physicians and students (Javaid and Haleem, 2020).

1. Cluster 3 (blue): Sustainability in Industry 5.0 with technology

The goal of Industry 5.0 is to improve human-machine collaboration by emphasizing human-centricity. In line with big data frameworks, it places a strong emphasis on incorporating cutting-edge technologies like artificial intelligence (AI), simulated reality, and user feedback to guarantee security, dependability, and efficient decision-making (Rožanec et al., 2022). Industry 5.0 made a more resilient, sustainable, and human-centered industrial system by merging the precision and effectiveness of machines with human creativity and problem-solving capabilities. The study describes how Industry 5.0 might be used in several industries, like manufacturing, supply chain management, and healthcare (Hozdić and Makovec, 2023).

Industry 5.0 emphasizes cooperation between humans and robots and takes a human-centric approach to manufacturing. It expands on Industry 4.0 technologies like automation, IoT, and AI while emphasizing human intellect, creativity, and personalization. Customizations and the demand for more robust and sustainable production methods are two issues that Industry 5.0 seeks to solve. In addition to improving the overall consumer experience and adding value through mass personalization, it uses cutting-edge technology like digital twins, collaborative robotics, and machine learning to increase industrial efficiency, accuracy, and responsiveness (Madsen and Berg, 2021).

Table 3 summarizes the bibliographic coupling analysis with cluster number and color, labels, number of publications, and representative publications. This table is according to the top 10 documents, which are used in clusters and are also shown in Figure 2.

By applying the same database, 59 out of 4,185 keywords presented through the co-word analysis met 13 thresholds, resulting in 4 clusters. The highest co-occurring keywords are “industry 5.0” (338 occurrences), “industry 4.0” (142 occurrences), and “artificial intelligence” (133 occurrences). Table 4 gives the top 15 keywords in the co-occurrence of keywords analysis.

Figure 5 presents the co-word analysis’s network structure. It visibly exhibits 4 clusters, which represent 4 different themes. Under the author’s inductive interpretation, the 4 clusters are assigned the proper labels.

1. Cluster 1 (red): Leveraging IoT and ML for Sustainable Digitalization

The evaluation of smart cities, which emphasizes the fusion of cutting-edge advancements and sustainability principles, signifies a paradigm shift in urban development. Advancement in Construction 4.0, based on Industry 4.0, and the use of Building Information Modeling (BIM), crucial for facilitating sustainable construction techniques, are at the heart of this shift (Chen et al., 2022). The Internet of Things (IoT) promotes sustainability through digitalizing processes like water distribution and intelligent manufacturing. However, IoT has a significant carbon footprint due to energy utilization and reliance on scarce raw materials. To diminish this, the Green IoT (G-IoT) paradigm aims to reduce its impact on the environment, though it faces challenges from Edge AI, which enlarges energy demands.

The article provides guidelines for developers to address sustainability summons when designing future Edge-AI G-IoT systems, balancing innovation with environmental consequences (Fraga-Lamas et al., 2021). Industry 5.0 is the next evolution of the Industrial Revolution, targeting a human-centric approach to technology. It aims to manage the benefits of automation with ethical considerations, social equity, and environmental sustainability (Özdemir and Hekim, 2018). Industry 4.0 has significant economic and ecological imputation, but its social impact remains uncharted. This research aims to fill this gap by conducting a systematic review and a machine-learning analysis of academic and grey literature (Grybauskas et al., 2022).

1. Cluster 2 (green): Advancing Industry and Society 5.0: Integrating Technology, Sustainability, and Human-Centric Approaches

Pillai et al. (2021) Concept of Hospitality 5.0, rooted in Industry 5.0, emphasizes leveraging technology of hygiene management and operational efficiency, inspired by lessons from past pandemics. By fostering human-machine collaboration, it aims to enhance service standards while ensuring cleanliness and efficiency at consumer touchpoints, providing theoretical and practical insights for a resilient hospitality industry. The COVID-19 pandemic has accelerated the shift from Industry 4.0 to society integration AI, big data, and IoT into key sectors like healthcare for personalized systems. And this highlights how AI and robotics improved pandemic responses, fostering human-machine collaboration and advancing a super-smart society (Sarfraz et al., 2021).

The evolving role of social competencies is becoming critical in supply chain in supply chain management, especially with the shift towards sustainability and the society 5.0 economy. Key social skills are essential for future managers to navigate dynamic industries like healthcare (Foltynowicz et al., 2024). SMEs can enhance operational performance by adopting inventory management practices, reducing costs, and improving efficiency. Leveraging technologies like IoT, big data, and machine learning enables streamlined supply chains and competitive adaptability (Panigrahi et al., 2024). Industry 5.0 introduced a human-centric approach, prioritizing environmental sustainability, resilience, and safe work environments. By integrating human elements with advanced technologies, Industry 5.0 addresses concerns about dehumanization, aiming for a balanced and inclusive economic ecosystem (Saniuk et al., 2024).

1. Cluster 3 (blue): Human-centered Innovation; Balancing Technology with Human Expertise

To assist organizations in implementing innovation more successfully, the text suggests a novel framework known as “Absolute Innovation Management (AIM)”. AIM makes advancements more accessible, helpful, and aligned with company objectives by fusing design thinking, innovation ecosystems, and corporate strategy. AIM seeks to promote economic growth and competitive advantage by emphasizing user-centered innovation and cutting-edge technologies like Industry 5.0 and the Internet of Things (Aslam et al., 2020). Automation and Industry 4.0 technologies are driving a digital transformation in the shipping sector, resulting in the creation of autonomous ships. This raises queries about the future role of sailors, even though it promises greater efficiency and safety. The significance of the human factor in this shift is emphasized in this research. It contends that Industry 4.0 undervalues human intellect while emphasizing automation.

The study suggests the transformation towards Industry 5.0, which places a higher priority on human-centered innovation, to address this. This could be called Maritime Education and Training 5.0 (MET 5.0), Maritime 5.0, Shipping 5.0, or Seafarer 5.0 in the maritime context. These ideas emphasize the necessity of striking a balance between automation and human knowledge and abilities to guarantee a secure and sustainable future for the maritime sector (Shahbakhsh et al., 2022). Industry 5.0 emphasizes human-centric collaboration, combining human skills with robotic assistance to enhance productivity and well-being. It integrates ergonomic design and optimization to create a safer, more efficient working environment (Meregalli Falerni et al., 2024). Digital Intelligent Assistants are transforming manufacturing by reducing cognitive workload, enhancing user experience, and improving operational efficiency. By leveraging AI, Digital Intelligent Assistants enable streamlined processes, support human-centric tasks, and foster greater productivity in industrial settings (Colabianchi et al., 2024).

1. Cluster 4 (Yellow): Human-Centric Revolution; Harnessing AI, IoT, and Blockchain

The next stage of the industrial revolution, Industry 5.0, is when people and robots work together harmoniously. Advances in brain-machine interfaces and artificial intelligence are driving this new era, which enables robots to collaborate with humans to increase efficiency and productivity. Industry 5.0 emphasizes human-centric production, prioritizing sustainability and worker well-being. With the precision and speed of robotics combined with human ingenuity and problem-solving abilities, Industry 5.0 seeks to build a more just and sustainable future (Nahavandi, 2019). Building on the achievements of Industry 4.0, Industry 5.0 represents the next stage of the industrial revolution. Industry 5.0 places a higher priority on human-machine collaboration than Industry 4.0, which is more concerned with automation and connection.

Industry 5.0 seeks to build a more resilient, sustainable, and human-centered industrial system by fusing the precision and efficiency of machines with human creativity and problem-solving abilities. The rapid growth of AI and Industrial Internet of Things (IIoT) has advanced hyper-automation in Industry 5.0, integrating intelligent devices, cloud computing, and smart robotics. However, cybersecurity threats like malware and abnormal activities pose challenges to hyper-automation, requiring an advanced detection system (Souri et al., 2024). The integration of AI in Industry 5.0 shifts the focus from how innovation drives AI to how AI drives innovations. Factors like company age, AI maturity, and manufacturing strategies influence this effect, with outcomes depending on their alignment (Bécue et al., 2024).

A summary of the co-word analysis is presented in Table 5, which presents the summary of the co-word analysis with cluster number, color, labels, number of keywords, and representative keywords. This table illustrates the primary keywords that are used in clusters.

This section presents the findings of the bibliometric and co-word examines concerning the existing literature, highlighting their broader implications for the advancement of Industry 5.0. It interprets the significance of the identified themes and clusters, emphasizing both their theoretical contributions and practical relevance for industry and policymakers. The study reveals several critical insights that are essential for deepening understanding and promoting the adoption of Industry 5.0 technologies. By integrating artificial intelligence (AI), machine learning (ML), and human-centered design, this research provides a comprehensive perspective on the future of sustainable, inclusive, and innovation-driven industrial practices. While the literature demonstrates substantial depth in topics such as predictive maintenance, collaborative robotics, and AI-enabled automation, the field appears oversaturated in these technical areas. In contrast, empirical investigations into social sustainability, such as workforce equity, ethical AI adoption, and inclusive design, remain relatively underexplored. This imbalance highlights an opportunity for scholars to expand Industry 5.0 discourse into the domains of ethics, well-being, and human development.

This study is explicitly anchored in socio-technical systems theory, which emphasizes the interdependence of social structures, workforce practices, and enabling technologies in sustainable and resilient Industry 5.0 systems (Chen et al., 2023). And human-centered design principles, which embed human values, usability, and ethics into AI and IoT development (Valette et al., 2023). The shift from traditional automation to human-centric innovation, expanding knowledge of Industry 5.0. It broadens the theoretical framework for understanding how advanced technologies like collaborative robots, predictive maintenance, and cyber-physical systems can derive sustainability, social responsibility, and economic resilience (Zafar et al., 2024). With an emphasis on finding answers to socioeconomic and environmental problems that Industry 4.0 failed to address adequately, Industry 5.0 aims to meet the societal challenges brought on by the digital industrial transition. Its definition, scope, and technology context are not entirely clear, despite its potential (Ghobakhloo et al., 2023). Emphasizing ethics and sustainability, the research highlights the importance of value-sensitive design and ethical framework to guide human-centric collaboration, addressing challenges like equitable access, employment displacement, and technology ownership (Cawthorne and Robbins-van Wynsberghe, 2020).

Identifying gaps in the literature related to the social and ecological impact of Industry 5.0 encourages research into areas such as AI’s role in social sustainability, circular economy integration, and emerging technologies like Edge AI and Green IoT. The study takes a multidisciplinary approach, spanning sustainability, computer science, and industrial engineering, and fosters exploration through perspectives like socio-technical system theory and the political economy of technological adaptation. The positioning of Green IoT and Edge AI within the co-word clusters reveals a notable shift in Industry 5.0 discourse from viewing these tools as standalone technologies to framing them as drivers of sustainability-oriented structure (Longo et al., 2020). This reinforces emerging theoretical approaches like value-sensitive design and extends socio-technical frameworks into the environmental and ethical domains. Global value chain (GVC) participation, business competitiveness, and sustainable business growth of SMEs in Thailand’s marine food processing sector are all positively impacted by Industry 5.0 readiness. The relationship between Industry 5.0 preparedness and sustainable business growth is mediated by business competitiveness (Madhavan et al., 2024). Alojaiman (2023) study looks at how Industry 5.0 might be used in several industries, such as manufacturing, supply chain management, and healthcare. It also explores the difficulties and constraints of putting Industry 5.0 into practice, like the requirement for highly qualified workers and cutting-edge technologies.

Ben Youssef and Mejri (2023) examine how Industry 5.0 uses AI and IoT to solve environmental issues while encouraging ethical thinking, human-centered innovation, and resilience. It draws attention to important lines of inquiry in this area. The new industrial revolution, known as “Industry 5.0,” is centered on sustainability and human-centered innovation. By using technologies like blockchain, IoT, and AI to improve human capacities and develop more robust and moral systems, it expands on Industry 4.0. Because blockchain technology offers an auditable, transparent, and unchangeable record of data and transactions, it can be especially important in protecting Industry 5.0 ecosystems. This can assist in addressing issues including supply chain security, data privacy, and intellectual property protection (Verma et al., 2022). Nicholas (2024) investigates how Industry 5.0 stresses striking a balance between sustainability principles and digital transformation. This strategy encourages creativity and a socioeconomic environment that combines advancements in technology with the welfare of society. To illustrate the theoretical integration of key technologies within Industry 5.0, Figure 6 summarizes the role of AI, ML, and IoT in enabling sustainable and human-centered outcomes.

Given the consistent presence of AI and real-time monitoring across both bibliometric and co-word clusters, it is evident that digital intelligence has become a cornerstone of resilient and adaptive post-pandemic supply chains. This trend demands a strategic shift toward predictive, competency-driven decision-making frameworks within organizations. The transformative role of AI and ML in enhancing real-time decision-making, reducing costs, and aligning with goals like waste reduction and energy optimization has been discovered. It highlights the need for businesses to prepare for Industry 5.0 by fostering a culture of innovation and agility, equipping employees with advanced skills, and leveraging collaborative robots. AI-based solutions have demonstrated their value in building supply chain resilience, particularly in volatile post-pandemic environments, by enabling real-time monitoring and predictive analytics. Recent systematic reviews have emphasized the transformative role of information technologies such as IoT, Big Data, AI, and Blockchain in supply chain performance and resilience, although their application remains underexplored in current practice (Al-Talib et al., 2024). The integration of Lean Office (LO) principles into project management has also been recognized as a promising strategy to streamline operations, reduce waste, and enhance overall performance in industrial and administrative processes (Martins and Frederico, 2024). Recent scoping reviews have also emphasized that digital technologies, when applied to food supply chains, can enhance sustainability, improve traceability, and support the resilience of SMEs, especially in unorganized sectors (Panigrahi et al., 2024). The report advocated human-centric design to improve productivity and sustainability, promoting cooperation between workers and smart devices while adhering to ethical practices (Rame et al., 2024). It underscores the strategic importance of sustainability through energy-efficient technologies and green IoT, improving environmental impact and market competitiveness. Industry 5.0 also creates opportunities for mass customization, enhancing customer satisfaction and competitive advantage. In healthcare, it enables personalized treatments, efficient emergency responses, and improved patient outcomes through technologies (Ugo-Njoku, 2023).

By enhancing human-machine interaction and utilizing AI and machine learning for more intelligent manufacturing, Industry 5.0 focuses on meeting the needs of individual customers. It improves industrial automation with human intelligence and gets rid of monotonous work. To increase manufacturing’s value and consumer pleasure, the revolution presents 17 essential elements that promote precision, efficiency, and mass personalization (Javaid and Haleem, 2020). Governments and society are concerned about the consequent dehumanization of the industry as a sequel to the 4th industry automation’s rapid expansion, which is centered on the adoption of 4th industry advancements. To make predictions about the development of industry, it is currently necessary to take sustainable development and the crucial role of humans. Industry 5.0’s fundamental presumptions were based on worries about using the advancements from the 4th industrial revolution. In light of the maturing of Industry 4.0’s sustainability, humanization, and resilience, the essay seeks to identify the social and economic presumption of Industry 4.0 (Saniuk and Grabowska, 2022).

For small and medium-sized enterprises (SMEs), the adoption of Industry 5.0 technologies such as AI-enabled predictive analytics and collaborative robotics can enhance operational resilience and mass customization, leading to increased competitiveness and reduced production costs (Panigrahi et al., 2024). In the healthcare sector, Industry 5.0 enables real-time patient monitoring and personalized treatment through AI and IoT integration, improving health outcomes and supporting inclusive care systems that enhance societal well-being (Javaid and Haleem, 2020). Beyond immediate operational benefits, the insights from this study support policy development, future academic inquiry, and workforce training aligned with Industry 5.0 principles. For researchers, the identified gaps suggest a need to further investigate social equity, digital ethics, and value-driven technology adoption. Adoption of human-centric technologies has potential for society in the form of more ethical workplaces, inclusive educational models, and improved healthcare services.

This study explores the transformation from Industry 4.0 to Industry 5.0 by employing bibliographic coupling and co-word analysis to examine scholarly developments between 2010 and 2024. It provides a comprehensive overview of how advanced technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) are shaping the emergence of human-centric, sustainable, and resilient industrial systems. Theoretically, the research advances the understanding of Industry 5.0 by reframing it as a socio-technical shift that emphasizes ethical technology adoption, sustainable practices, and inclusive innovation. It builds on existing knowledge by proposing that next-generation manufacturing and services must align with societal values and environmental goals, expanding frameworks like value-sensitive design and the socio-technical systems approach.

On a practical level, the findings reveal the growing role of AI-driven tools and real-time data analytics in improving decision-making, operational efficiency, and sustainability across various sectors. Managers and policymakers can leverage these insights to foster adaptive strategies, invest in workforce upskilling, and promote responsible human-machine collaboration. The integration of smart technologies with human insight is shown to improve not only productivity but also worker well-being and ethical standards in design and implementation. This study has implications beyond academic understanding. It encourages industry leaders and policymakers to shift from purely efficiency-based automation toward strategies that balance technology with human needs and ethical concerns. Additionally, it sets a foundation for cross-sector adoption of Industry 5.0, with particular relevance to developing economies, SMEs, and public service systems aiming to implement sustainable innovation.

There are three primary limitations to acknowledge. Methodologically, the analysis is based solely on data from the Scopus database, without incorporating additional sources or empirical validation. In terms of scope, the focus is restricted to AI, ML, and IoT within Industry 5.0, excluding other emerging technologies such as blockchain and quantum computing. Regarding generalizability, the results may not fully capture regional or industry-specific variations, particularly in underrepresented or developing contexts. This research contributes to theory by strengthening the application of socio-technical and human-centered design frameworks within the context of Industry 5.0. It also offers practical insights for organizational leaders to implement AI- and IoT-enabled innovation in real-world environments. The methodological approach of combining bibliometric and co-word analysis demonstrates how scientific mapping can inform strategic decision-making in rapidly evolving domains.

Future research should prioritize empirical studies to substantiate these findings in practical contexts. Case studies across industries and countries can provide richer insights into how Industry 5.0 technologies are deployed and what challenges they encounter. Additionally, emerging technologies such as blockchain, augmented reality, and quantum computing offer promising avenues for further exploration, particularly in addressing ethical dilemmas, data security, and inclusive design. Research integrating perspectives from psychology, sociology, and environmental science could deepen our understanding of how Industry 5.0 impacts human well-being, organizational culture, and ecological balance. Investigating how AI and smart systems can reduce environmental footprints, support circular economy models, and improve disaster resilience would also provide valuable contributions.

Overall, this study sets the foundation for a deeper exploration of Industry 5.0, positioning it not just as a technological upgrade but as a transformative movement that reimagines the future of work, sustainability, and digital ethics. A successful transition to this new paradigm will require interdisciplinary collaboration and a strong commitment to aligning innovation with broader societal goals. The findings also have implications for industry practice and public policy. Organizations can use the identified thematic clusters to guide technology investment, workforce upskilling, and human-centric innovation strategies. Policymakers may leverage these insights to design supportive frameworks that encourage responsible AI adoption, sustainable manufacturing, and inclusive digital transformation aligned with Industry 5.0 principles.

We have not used artificial intelligence (AI) tools to create this article.

The Universiti Malaysia Pahang Internal Grant funds this study by Grant No RDU230307.