Assessment and Insights into the Awareness and Readiness of Organizations to Implement the Assumptions of Industry 5.0: An Examination of Five Polish Sectors

Abstract

The aim of this study is to assess the level of awareness and readiness of organizations to implement the assumptions of Industry 5.0, as well as to identify the benefits and challenges associated with this process. The paper makes an original contribution by combining empirical analysis with the proposal of a practical model, enabling a better understanding of the technological and social transformation process in Polish organizations. The article presents an original model for implementing the assumptions of Industry 5.0, integrating technological, social, and organizational aspects, offering a comprehensive approach to transformation towards sustainable and human-centered development. The study was conducted among 556 Polish companies from five sectors: IT, automotive, industrial, service, and banking/financial, using a non-random sampling method and data analysis through techniques such as association rules and hierarchical clustering. The research results indicate that most organizations are familiar with the basics of the Industry 5.0 concept (25% full knowledge, 66% partial knowledge), but only a portion is engaged in the transformation process (59%), which typically takes place gradually (53%). The most commonly reported benefit of Industry 5.0 by organizations was improved product and service quality (73%), while the most frequently cited challenges included the need for staff training (58%), ensuring data and network security (53%), and modernizing infrastructure and systems (52%). Benefits such as improved product quality, increased production efficiency, and cost optimization are primarily recognized by companies in the IT and industrial sectors. At the same time, challenges such as the need to modernize infrastructure and ensure data security, as well as implementation costs, remain significant barriers, particularly for small- and medium-sized enterprises. The research findings have practical significance as they provide companies and decision-makers with guidance on effective planning and implementation of actions related to the implementation of Industry 5.0.

1. Introduction

Industry 5.0 is emerging today as a new paradigm for managing physical, intellectual, and technological resources, becoming the key to harmonious, human-centric development. This concept signifies the skillful integration of technological progress with human well-being and sustainable growth. In Industry 5.0, recognized as a new economic model, the main emphasis is placed on close collaboration between humans, machines, and digital technologies [1,2,3]. The key aspects of this collaboration include: (1) complementarity—machines should enhance human skills; (2) security and privacy; (3) flexibility and adaptability of systems to user needs; (4) education and training; (5) humanization of technology; and (6) ethics and responsibility [4]. The technologies driving digital transformation and directly influencing the development of Industry 5.0 include [1,5,6,7,8,9]: (1) Generative Artificial Intelligence (GAI), enabling the creation of new, unique content such as text, images, music, and film; (2) AI and machine learning, along with big data analytics, as tools for analyzing data, predicting trends, and personalizing offerings; (3) The Internet of Things (IoT), enabling automation and remote management of processes in production, logistics, agriculture, and more; (4) Robotics and automation, which take over routine, repetitive tasks, allowing employees to focus on more creative and skill-intensive tasks; and (5) Blockchain technology, facilitating secure data exchange and transaction verification, which is crucial in finance, supply chains, and identity management.

The aforementioned technologies are becoming vehicles for economic and social transformation, fostering the emergence of innovative business models, highly personalized services, and communication, as well as the detection of trends, customer behaviors, and environmental anomalies and threats [6]. For researchers and practitioners engaged with these topics, this implies the need to develop new business models based on sustainable collaboration between companies, employees, consumers, and other stakeholders in socio-economic life.

Understanding the mechanisms occurring at the intersection of economy, people, machines, and education—hallmarks of Industry 5.0—requires in-depth scientific research. So far, strategies, principles, and guidelines for policymakers, entrepreneurs, and employees on how to leverage advanced technologies for the development of Industry 5.0 have not been developed. The lack of such guidelines may lead to inefficient and fragmented actions as well as potential risks, such as digital exclusion or social inequalities [10].

The theoretical aim of this study is to deepen knowledge about Industry 5.0, a concept that responds to the challenges posed by the dynamic development of technology and societal changes. The empirical aim is to examine the level of awareness regarding the development of Industry 5.0 within organizations, the benefits of its implementation, and the challenges organizations face in this complex process. This study investigates the level of awareness and readiness of Polish enterprises to implement Industry 5.0 principles, offering a novel perspective on the technological and organizational transformation towards sustainable and human-centric development. Unlike existing research, this paper provides a detailed examination of the interplay between sectoral characteristics, organizational size, and capital structure in shaping Industry 5.0 adoption. Achieving presented objectives required the application of various research methods, including a critical literature review and conducting surveys among representatives from five sectors: IT, automotive, service, banking/financial, and industrial. The five highlighted sectors differ in their contribution to Gross Domestic Product (GDP), number of employees, years of existence, and capital structure.

The service sector dominates the Polish economy, contributing 57.6% to GDP and employing 10.8 million individuals. The industrial sector, despite contributing 23.5% to GDP, employs 3.2 million people. IT and automotive sectors each contribute 8% to GDP, with employment figures of 0.43 million and 0.2 million, respectively. The banking/financial sector contributes 5.3% to GDP, employing approximately 0.15 million individuals [11,12,13,14,15,16,17].

The average years of existence vary across sectors. Industrial firms typically operate for 12 years, reflecting a stable but dynamic environment. In the IT sector, defining longevity is challenging due to varying dynamics; individual entrepreneurs in IT and communications have an average activity span of 5.6 years, while firms producing computers, electronics, and optical products average 17.1 years. In the automotive sector, available data suggests businesses in trade and vehicle mechanics function for 12.2 years on average. The service sector shows an average firm lifespan of 9.5 years [18]. In contrast, the banking/financial sector’s longevity is influenced by strict regulatory frameworks, capital requirements, and oversight by institutions like the Polish Financial Supervision Authority (KNF), making precise estimation difficult. It is worth emphasizing that some of the companies have a hundred-year tradition (e.g., Bank Pekao S.A.), while others are new companies operating on the market.

Ownership structures in these sectors display significant diversity. The industrial sector is predominantly driven by domestic capital, with foreign investment playing a crucial role in manufacturing, where it represented 39% of total foreign capital in Poland in 2022 [19]. The IT sector lacks comprehensive data regarding ownership distribution; however, in 2020, foreign capital accounted for PLN 18.4 billion, or 7.9% of foreign investments in the broader “Information and Communication” sector [20]. Despite the dynamic nature of the IT industry, the contribution of foreign investment remains significant in certain subsectors. In the automotive sector, foreign capital dominates, particularly among large enterprises, which generate 92% of the industry’s revenue [19]. Smaller firms, often domestically owned, primarily focus on producing parts and accessories, demonstrating the segmented nature of capital ownership in this industry. The capital structure of the service sector in Poland is diverse and varies depending on the specific industry segments. In certain areas, such as modern business services, there is a significant share of foreign capital. According to data from the Association of Business Service Leaders (ABSL), there are over 1941 business service centers in Poland, most of which are foreign investments [21]. On the other hand, in traditional services such as retail, gastronomy, and transport, enterprises with domestic capital dominate. In the banking/financial sector, ownership is notably diverse. The banking sector in Poland exhibits a predominantly domestic ownership structure, with 57.7% of the capital being domestic and 42.3% foreign [17]. This indicates a significant domestic dominance in the sector’s capital composition. Finally, the financial sector as a whole lacks precise data delineating the dominance of specific ownership types. The sector’s structure varies significantly across its segments, influenced by differing levels of regulation and market dynamics.

The first part of the article presents the key principles of Industry 5.0, taking into account its technological, social, and economic contexts. It then discusses the objectives of the empirical research, the research methodology employed, and the most significant findings and conclusions derived from the study. The empirical section provides detailed results from research conducted on a sample of N = 556 selected organizations. These studies illustrate the level of awareness and readiness for the implementation of Industry 5.0 in the examined economic sectors. Finally, the article outlines directions for future research and provides recommendations for implementation strategies in light of the challenges that Industry 5.0 poses for contemporary organizations. A mind map, showing the structure of the paper, is shown in Figure 1.

2. Theoretical Foundation and Hypotheses

2.1. The Essence and Concept of Industry 5.0

Industry 5.0, also known as Society 5.0, is a concept that integrates advanced technologies with various aspects of social and economic life, aiming to create harmonious and sustainable development. This idea arose from the need to address the challenges of the modern world, including demographic changes, urbanization, climate change, and growing social and economic inequalities [4,22]. The concept of Industry 5.0 involves leveraging the latest advancements in artificial intelligence (AI), the Internet of Things (IoT), robotics, big data, and blockchain technology to build more integrated, efficient, and inclusive societies. In contrast to Industry 4.0, which focuses primarily on automation and digitization of production processes, Industry 5.0 emphasizes the central role of humans in the economic and social ecosystem [3].

An essential element of Industry 5.0 remains the concept of sustainable development, which aims to achieve harmony between economic growth and environmental protection, transitioning from a model where profit and efficiency are the primary goals to one that prioritizes human and environmental well-being [23]. Technology is envisioned as a supporting tool rather than a dominant force, leading to the creation of new business models that integrate social and ecological needs. Industry 5.0 also emphasizes significantly increasing social participation in decision-making processes. Thanks to digital technologies, citizens are expected to have greater influence over shaping policies and development strategies [24]. These principles contribute to increased transparency and accountability in public governance. In this way, Industry 5.0 promotes a more democratic and sustainable approach to social and economic development.

A key principle is also inclusivity, aiming to reduce social and economic inequalities. Within Industry 5.0, technologies are intended to be accessible to all, ensuring equal opportunities for development and participation in the labor market for various social groups, including the elderly and people with disabilities [2]. Industry 5.0 can thus be described as a vision of the future where advanced technologies are employed in service of humanity, promoting sustainable development, inclusivity, and greater social participation. It represents an economic model that seeks the harmonious coexistence of technology, society, and the natural environment, emphasizing human well-being as the central focus of development [10].

The evolution of the concept of Industry 5.0 is a process that reflects the growing role of technology in shaping modern societies. Initially, concepts related to the industrial revolution focused primarily on mechanization (Industry 1.0), electrification (Industry 2.0), automation (Industry 3.0), and digitalization (Industry 4.0) [3,4,23,25]. Each of these stages introduced significant changes in how societies produced goods and services, as well as in social and economic structures [3,26,27,28,29].

Industry 5.0 emerged as a response to these challenges. This concept was formally introduced by the Japanese government as part of its “Society 5.0” policy, which was first outlined in the “Fifth Science and Technology Basic Plan” in 2016. The goal was to create a society that harmonizes technological development with social and environmental needs [25]. Industry 5.0 therefore places significant emphasis on the centrality of humans within the economic ecosystem, striving for sustainable development that not only promotes technological innovation but also integrates social well-being and environmental protection. It represents a more holistic approach, envisioning technologies such as artificial intelligence, the Internet of Things, robotics, big data, and blockchain as tools to address global challenges such as climate change, aging populations, and social and economic inequalities [4].

Industry 5.0 is a seamless transition from Industry 4.0. Industry 4.0 and Industry 5.0 represent successive phases in the evolution of industrial manufacturing, each with distinct focuses and objectives. The transition from Industry 4.0 to Industry 5.0 marks a shift in focus and priorities within industrial manufacturing. While Industry 4.0 aims to reduce human intervention through the extensive use of automation, Industry 5.0 reintroduces humans as key participants in the process, emphasizing collaboration between humans and machines. The objective of Industry 4.0 revolves around achieving efficiency and productivity, leveraging advanced technologies to optimize operations. In contrast, Industry 5.0 shifts towards personalization, sustainability, and human-centric solutions, prioritizing the creation of more tailored products and environmentally responsible practices. Both phases rely on cutting-edge technologies; however, Industry 5.0 builds upon the digital infrastructure established by Industry 4.0, enhancing it to foster deeper human–machine collaboration and promote sustainable practices that address broader societal needs [30,31,32,33].

Industry 4.0 and Industry 5.0 use similar products (e.g., IoT, AI, big data, VR/AR) but they differ in their approach to their application, which affects how they are implemented and the goals they are intended to achieve. The main difference between Industry 4.0 and Industry 5.0 products is that while Industry 4.0 focuses on automation and system integration to enhance efficiency and productivity, Industry 5.0 emphasizes human-centric design, sustainability, and personalized solutions through collaboration between humans and machines [34,35,36]. For example, the IoT in Industry 4.0 is utilized for the automation and integration of production processes, enabling machine-to-machine communication and real-time data collection to optimize efficiency. In contrast, in Industry 5.0, IoT supports human–machine collaboration by providing data that aids decision-making and adapts processes to individual needs, with a focus on personalization and production flexibility [33,37]. It should be emphasized that some technologies used by Industry 4.0 and Industry 5.0 are different, e.g., autonomous robots and collaborative robots–cobots, respectively. Autonomous robots improve productivity, produce high-quality products at low prices, and meet customer expectations [38] while cobots play a pivotal role in enhancing human–machine collaboration by working alongside humans to perform tasks with precision, safety, and flexibility, bridging the gap between automation and human creativity [39].

Among specific examples of Industry 5.0 technology applications are exoskeletons that enhance accessibility for the elderly and disabled while supporting healthcare workers in their daily tasks, drones used for delivering medications, blood, or medical equipment to inaccessible areas [40], medical implants, artificial organs, bodily fluids, and transplants manufactured with millimeter precision to meet the specific needs and preferences of patients [41], and reinforcement learning, which can enable personalized treatment for chronic diseases [42].

The symbiotic relationship between humans and machines not only optimizes workflow but also enables product personalization to better meet social needs and preferences. Examples of successful implementations include the “smart factories” of automotive companies like Audi and Volkswagen, which emphasize improving human–robot interaction to enhance production flexibility [43].

To enhance sustainability, Industry 5.0 leverages advanced technologies like AI to optimize resource utilization. The integration of intelligent, connected machines and machine learning enables precise real-time production forecasting, allowing industries to dynamically adjust operations, thereby preventing waste and increasing efficiency. An example of this in action is BMW’s iFACTORY, which commits to sustainable manufacturing through the use of environmentally friendly materials, renewable energy, and extensive recycling practices [44].

Agriculture 5.0 (part of the broader concept of Industry 5.0) builds upon the advancements of Agriculture 4.0 by integrating cutting-edge technologies such as AI, IoT, 6G connectivity [45], digital twins [46], and cobots [47,48]. These technologies aim to create a more sustainable, efficient, and resilient agricultural system. For instance, AI and big data analytics enable precision farming, optimizing resource usage and increasing yields by providing farmers with detailed insights based on extensive datasets [47].

One of the key elements in the evolution of Industry 5.0 is the idea of smart cities, which leverage modern technologies to enhance the quality of life for residents. These cities are designed to be sustainable and environmentally friendly, using technology to optimize the management of resources, energy, transportation, and urban infrastructure. The central goal of this concept is to create environments that are more integrated, efficient, and resident-friendly [49]. The concept of Industry 5.0 also envisions the development of new business models that integrate sustainability and social responsibility. Companies are encouraged to adopt strategies that not only maximize profits but also contribute to improving societal well-being and protecting the environment. In this way, Industry 5.0 promotes an approach in which sustainable development becomes an integral part of business strategies [23]. The formation of the Industry 5.0 concept reflects a shift from traditional economic models focused on production and efficiency to a more holistic approach that integrates technology with social and environmental needs.

While Industry 5.0 offers tremendous benefits, it also raises numerous concerns, challenges, and potential threats, including ensuring robust security and data privacy in managing vast datasets and protecting sensitive information, especially in sectors like healthcare. The integration of human–robot collaboration requires reorganizing workflows and upskilling workers to interact effectively with advanced technologies. Efficient data management is essential to handle the large volumes, speeds, and types of data generated, alongside ensuring scalability in hyper-connected environments [33,50]. Ethical and legal challenges also arise, necessitating the establishment of strong frameworks for data protection and lawful usage, e.g., modern technologies can also be used for negative purposes, as demonstrated by the war in Ukraine, where commercial drones have been repurposed as weapons [40].

2.2. The Level of Awareness Among Enterprises Regarding the Implementation of Industry 5.0 Principles

Assessing the level of awareness among enterprises regarding the implementation of Industry 5.0 principles forms the foundation for effectively conducting the transformation process. In the literature, awareness is defined as the ability to recognize the potential of new concepts, their possible applications, and the challenges organizations may face during implementation. In the case of Industry 5.0, particular emphasis is placed on understanding its multidisciplinary nature, which encompasses not only technological aspects but also social, environmental, and ethical dimensions [51].

Theoretical foundations suggest that enterprise awareness can be considered at several levels. The first level is basic knowledge of the concept, which includes general information about the principles of Industry 5.0, such as the use of artificial intelligence, robotics, and big data to achieve sustainable development. The second level is an in-depth understanding that enables enterprises to perceive potential benefits, such as improving product quality or optimizing operational processes. The third and highest level is strategic awareness, allowing organizations to formulate long-term transformational plans tailored to the specifics of their activities [26].

The degree of engagement in the transformation process toward Industry 5.0 depends on many factors, including the level of awareness, available resources, and an organization’s readiness to take risks associated with innovation adoption. The literature identifies three stages of enterprise engagement. In the first stage, organizations begin exploring the opportunities offered by Industry 5.0 implementation, often reflected in market research and expert consultations [49]. The second stage involves actively implementing new technologies and processes to integrate innovations into the company’s daily operations. The final stage is the consolidation of changes, including continuous monitoring of outcomes and adaptation to dynamically changing market conditions [24].

The pace of implementing Industry 5.0 in enterprises is another critical aspect discussed in the literature. This pace depends on factors such as the availability of technology, the readiness of management to invest in new solutions, and the organizational structure of the enterprise. A rapid implementation pace can offer advantages like gaining a competitive edge but often comes with greater operational risks. Conversely, a gradual pace of transformation allows for better alignment of actions to the enterprise’s specifics and more efficient resource management, though it may delay the realization of the full benefits of the implemented changes [22].

Issues related to awareness, engagement, and the pace of implementing Industry 5.0 in enterprises reveal significant interdependencies between these factors. A higher level of awareness typically leads to greater engagement in the transformation process, which, in turn, influences the efficiency and speed of innovation adoption.

2.3. Hypotheses

Based on the theoretical analysis and preliminary empirical data, research hypotheses were formulated.

H1. 

Enterprises in technologically advanced sectors, such as IT and industrial sectors, demonstrate higher awareness and faster implementation of Industry 5.0 principles compared to less technology-focused sectors.

H2. 

Small and medium enterprises experience slower implementation of Industry 5.0 principles due to financial constraints, infrastructure limitations, and skill gaps compared to medium and large enterprises.

H3. 

The pace of Industry 5.0 implementation is influenced by sector-specific factors, with the IT sector leading in speed and efficiency compared to service and banking/financial sectors.

H4. 

Enterprises established after 2010 demonstrate greater adaptability and faster progress in Industry 5.0 implementation compared to older enterprises due to flexibility in adopting modern technologies.

To verify these hypotheses, the following research questions were formulated: (1) What is the current understanding of the Industry 5.0 concept among Polish enterprises? (2) To what extent are enterprises engaged in the transformation process toward Industry 5.0? (3) What pace of implementation of Industry 5.0 principles can be observed in the studied enterprises? (4) What factors have the greatest impact on awareness, engagement, and the pace of transformation toward Industry 5.0? (5) How do the specifics of the sector of activity and company size affect the effectiveness of implementing Industry 5.0 principles? (6) What benefits and challenges do enterprises identify during the transformation process? (7) What implementation strategies are most effective in increasing the pace and efficiency of transformation toward Industry 5.0?

3. Methodology

3.1. Data Collection and Sample

The objective of the conducted research was to examine the awareness of Industry 5.0 principles, the level of transformation, as well as the challenges and barriers associated with its implementation. The research was carried out among Polish organizations from five sectors: IT, automotive, industrial, service, and banking/financial services. The study was conducted between August and October 2024.

A non-random sampling method was applied in the study. The sample included N = 600 organizations, of which N = 556 completed surveys were valid for analysis. The questionnaire was administered online using the CAWI method. A structured questionnaire was employed in the research, and the majority of respondents were representatives of senior management or middle management. They were invited to answer 16 questions related to topics such as (1) The level of awareness of the Industry 5.0 concept. (2) Whether the organization is engaged in the transformation process toward Industry 5.0. (3) The pace of Industry 5.0 implementation within the organization. (4) The benefits or improvements associated with Industry 5.0. (5) The challenges encountered during the transformation process toward Industry 5.0 within the organization.

3.2. Data Analysis

The study employed association rules and hierarchical clustering on principal components (HCPC) as the primary data analysis methods. Association rules, an unsupervised machine learning method, were applied to identify patterns and relationships among enterprise characteristics—such as sector, size, year of establishment, and capital structure—and their levels of awareness and engagement in Industry 5.0 transformation. This approach facilitated the detection of co-occurring attributes, offering actionable insights into organizational readiness to implement the assumptions of Industry 5.0. Hierarchical clustering, combined with principal component analysis, was used to classify enterprises into homogeneous groups, which revealed sectoral differences and unique challenges faced by different types of organizations.

To identify specific categories of enterprises linked to comprehensive awareness of the Industry 5.0 concept, engagement in the transformation process, and its implementation, association rules were utilized. An association rule takes the form X- > Y, where X (antecedent, left-hand side—LHS) is a set of items, and Y (consequent, right-hand side—RHS) is an item. The implication of the rule is that if all elements in X are present in a particular “basket”, Y is “likely” to also appear in the basket. In this context, if an enterprise possesses certain characteristics, it is likely to implement Industry 5.0.

To identify strong rules, metrics such as support, confidence, and lift were used. Support indicates the frequency of occurrence of an itemset, enabling the identification of frequent sets. Confidence represents the conditional probability of the consequent given the antecedent. Lift measures how many times more often X and Y occur together than expected if they were statistically independent. A lift of 1 indicates no dependency between X and Y; lift > 1 suggests that X and Y are likely to occur together, while lift < 1 indicates that X and Y are unlikely to occur together. Lift, therefore, serves as a measure of the “strength” of a rule. A detailed description of association rules can be found in the work of Agrawal et al. [52].

The apriori algorithm was used to extract association rules, leveraging prior knowledge about the properties of frequent itemsets in the rule-generation process. This algorithm operates iteratively, identifying frequent itemsets by progressively increasing their size while ensuring they meet the specified thresholds for support and confidence. To assess the statistical significance of the generated association rules, Fisher’s exact test was applied (assuming p = 0.05). The algorithm’s efficiency lies in its ability to prune the search space by eliminating infrequent itemsets early in the process, significantly reducing computational complexity. Redundant rules—those for which an additional element on the left-hand side did not improve confidence—were removed [53]. This refinement step ensures the final set of rules is both concise and interpretable, facilitating actionable insights from the data.

To identify patterns and relationships between enterprise categories and the benefits and challenges encountered during the transformation toward Industry 5.0, hierarchical clustering on principal components was employed. Since the variables included in the analysis are qualitative, multiple correspondence analysis (MCA) was first conducted. MCA reduced the dimensionality of the dataset by identifying principal components that capture the most significant variance in the data. Then the object coordinates on the principal components were used for hierarchical clustering (HC), which was performed using Ward’s method By leveraging MCA as foundation, the analysis ensures that clusters are based on the most informative aspects of the dataset. MCA was thus used as a preliminary step to transform qualitative variables into continuous variables [54], enabling the application of hierarchical clustering techniques.

Although the study focuses on a specific context—Polish enterprises across five sectors—the scale of the research (556 companies), the use of advanced data analysis methods, and the attempt to generalize findings indicate that this work is not a traditional case study. Rather than examining a single instance, this study provides a comprehensive analysis aimed at identifying general patterns in the readiness of enterprises to implement Industry 5.0 principles.

Most calculations and visualizations were performed in R [55] using the arules [56], arulesViz [57], and DiagrammeR [58] packages. The exception is the Sankey diagram, which was created using SPSS [59].

4. Results

4.1. Sample Characteristics

Table 1. Sample characteristics (N = 556).

Variable	Category	%
Sector	Banking/financial	18.5
	IT	21.9
	Automotive	20.0
	Industrial	18.3
	Service	21.2
Size	10–49 employees (small)	69.6
	50–249 employees (medium)	21.2
	250 and more employees (large)	9.2
	1989 and before	8.1
Year	1990–1999	18.0
	2000–2009	31.3
	2010–2019	36.0
	2020 and after	6.7
Capital	Polish	89.4
	Foreign	4.0
	Mixed	6.7

presents the structure of the surveyed enterprises according to their sector of activity, size, year of establishment, and type of capital. The conducted research indicated that the majority of companies operate in the IT sector (21.9%) and the service sector (21.2%). The vast majority are small enterprises (69.6%). Most of the companies were established between 2000 and 2009 (31.3%) or between 2010 and 2019 (36.0%). Polish capital predominates (89.4%), with a smaller share of foreign capital (4.0%) and mixed capital (6.7%).

4.2. Awareness, Commitment, and Pace of Implementation of Industry 5.0

The surveyed enterprises were asked about their awareness of the Industry 5.0 concept, their commitment to the transformation process toward Industry 5.0, and the pace of implementation of Industry 5.0 within their organizations. A graphical representation of the responses to these three questions is shown in Figure 2.

The answer to the first research question is provided by the observation that the majority of enterprises have heard of Industry 5.0 but acknowledge that they lack comprehensive knowledge of the concept (66%). Only one in four enterprises (25%) is fully aware of the Industry 5.0 concept. One in eleven enterprises (9%) is entirely unfamiliar with Industry 5.0.

Answering the second research question, among enterprises fully aware of Industry 5.0, almost half actively participate in the transformation (representing 12% of all surveyed enterprises), while the other half (also 12%) are in the early stages of transformation. Among enterprises with incomplete knowledge of Industry 5.0, most are at the beginning of the transformation process (33%), slightly fewer are considering such efforts (28%), and one in twenty-five enterprises is not considering engagement in the transformation process. Enterprises entirely unfamiliar with the Industry 5.0 concept mostly do not engage in the transformation process (6%), as they perceive it as irrelevant, while a smaller portion (3%) considers future involvement.

Overall, the majority of enterprises (59%) are actively involved or in the initial stages of transformation. Nearly one-third of enterprises (31%) are considering engagement in the transformation process toward Industry 5.0. Only 10% of enterprises do not plan to engage in the transformation process, considering it irrelevant to their operations.

Among enterprises actively involved in the transformation process, most describe the pace as gradual (8% of all enterprises), while a significant portion indicates the process is happening quickly (5%). Among enterprises in the early stages of transformation, most report that changes are occurring gradually (35%), although a notable proportion describes the pace as slow (9%). Among enterprises considering the implementation of Industry 5.0, the majority also describe the pace as slow (19%). For enterprises that view the transformation process toward Industry 5.0 as irrelevant, most have no opinion on the pace of implementation within their organization (65%).

Overall, the majority of enterprises characterize the pace of Industry 5.0 implementation as gradual (53%), while about one-third (32%) describe the pace as slow. Nearly equal proportions of enterprises report the pace as fast or have no opinion on the matter (7% and 8%, respectively). The presented results are a direct answer to the third research question.

To answer the fourth, fifth, and seventh research questions, association rules were identified. Examining the connection between the categories of enterprises included in the study and full awareness of the Industry 5.0 concept, the apriori algorithm identified three association rules, one of which was removed as redundant. Figure 3 presents a parallel coordinates plot for the two remaining rules. The bold, distinct red line indicates a strong association between a category and a higher probability of full awareness of the Industry 5.0 concept, while weak or almost invisible red lines suggest that the category is unlikely to increase the probability. The x-axis represents the position of each category within the rule.

Two association rules are statistically significant (based on Fisher’s exact test) and are listed with their corresponding lift and confidence values below.

• Rule 1: If sector = industrial, size = small, year = 2010–2019, then the probability of full awareness is 66.67% (lift = 2.71).
• Rule 2: If sector = industrial, size = medium, capital = mixed, then the probability of full awareness is 60% (lift = 2.44).

Both of the mentioned rules appeared with the same frequency.

To identify the categories of enterprises associated with active or emerging involvement in the transformation process towards Industry 5.0, the apriori algorithm was used, followed by the elimination of redundant rules. Ultimately, four rules were identified (Figure 4), among which Rule 2 occurred most frequently.

• Rule 1: If size = large, capital = mixed, then the probability of active/starting commitment is 70% (lift = 1.54).
• Rule 2: If sector = industrial, size = medium, year = 2000–2009, capital = Polish, then the probability of active/starting commitment is 69.23% (lift = 1.52).
• Rule 3: If year = 1990–1999, capital = mixed, then the probability of active/starting commitment is 66.67% (lift = 1.47).
• Rule 4: If sector = industrial, size = small, year = 2010–2019, then the probability of active/starting commitment is 66.67% (lift = 1.47).

It should be emphasized, however, that none of the mentioned rules are statistically significant. This means that, in the case of active or emerging involvement in the transformation process, no discernible pattern can be identified.

The application of the apriori algorithm to determine the categories of enterprises associated with a rapid/gradual pace of implementing Industry 5.0 allowed for the identification of 14 rules, 8 of which were deemed redundant. Among the remaining rules (see Figure 5), all are statistically significant, with Rules 2 and 3 appearing most frequently.

• Rule 1: If sector = IT, year = 2020_and_after, then the probability of quick/gradual pace of implementation of Industry 5.0 is 85.71% (lift = 1.63).
• Rule 2: If sector = industrial, size = small, then the probability of quick/gradual pace of implementation of Industry 5.0 is 83.33% (lift = 1.58).
• Rule 3: If sector = industrial, year = 2000–2009, then the probability of quick/gradual pace of implementation of Industry 5.0 is 80.65% (lift = 1.53).
• Rule 4: If sector = industrial, size = medium, year = 2000–2009, then the probability of quick/gradual pace of implementation of Industry 5.0 is 88.89% (lift = 1.69).
• Rule 5: If sector = industrial, size = small, year = 2000–2009, then the probability of quick/gradual pace of implementation of Industry 5.0 is 90% (lift = 1.71).
• Rule 6: If sector = industrial, size = small, year = 2010–2019, then the probability of quick/gradual pace of implementation of Industry 5.0 is 88.89% (lift = 1.69).

All six association rules support H1, indicating that companies from technologically advanced sectors demonstrate a higher pace of Industry 5.0 implementation. Additionally, Rule 1 supports H3, and all rules (except Rule 2) support H4.

4.3. Benefits and Challenges

To answer the sixth research question, the benefits and challenges of Industry 5.0 were identified. The question regarding the benefits of Industry 5.0 was a multiple-choice question. Most enterprises indicated (Figure 6) that Industry 5.0 contributes to improving the quality of products/services (73%), while approximately half of the surveyed enterprises pointed to increased production efficiency as a benefit (52%). Responses related to flexibility in production adapted to changing market needs (39%) and production cost optimization (30%) were indicated by a smaller percentage.

The question regarding the challenges encountered during the transformation process toward Industry 5.0 was also a multiple-choice question. Most enterprises indicated (Figure 7) that the challenges include the need for staff training (58%), ensuring data and network security (53%), and the necessity of modernizing infrastructure and systems (52%). Slightly less than half of the enterprises stated that acquiring new skills is a challenge (47%), while one-third of the enterprises identified the costs of implementing new technologies as a challenge (35%).

To identify the relationships between the indicated benefits and categories related to enterprise characteristics (sector, employment size, year of establishment, and company capital), an MCA analysis was conducted, and the results of the category projection onto two main axes are presented in Figure 8. Based on the proximity of the categories, it can be concluded that enterprises operating in the IT sector are more likely to recognize the benefit of increased production efficiency, while medium-sized enterprises in the industrial sector with mixed capital more often emphasize the benefit of production cost optimization. It can also be observed that enterprises in the banking/financial sector frequently do not perceive the benefit of increased production efficiency.

Based on the hierarchical clustering conducted using MCA, it can be concluded that all analyzed variables had a statistically significant impact on the composition of the clusters, with the enterprise sector having the greatest influence (

Table 2. Benefits of Industry 5.0: Description of the clusters by variables.

Variables	p-Value	df
Sector	<0.001	8
Size	<0.001	4
Efficiency	<0.001	2
Capital	<0.001	4
Year	<0.001	8
Optimization	<0.001	2
Quality	<0.001	2
Flexibility	<0.001	2

Note: df = degrees of freedom.

).

Table 3. Benefits of Industry 5.0: Description of the clusters by categories.

Cluster	Cla/ Mod	Mod/ Cla	p-Value	v-Test
Cluster 1
sector = IT	91.803	82.353	<0.001	18.975
size = small	34.884	99.265	<0.001	10.125
efficiency = efficiency_efficiency	40.070	84.559	<0.001	9.152
optimization = optimization_	34.134	91.912	<0.001	6.935
year = 2010–2019	41.000	60.294	<0.001	6.668
capital = Polish	27.364	100.000	<0.001	5.596
quality = quality_quality	28.922	86.765	<0.001	4.233
flexibility = flexibility_	29.080	72.059	0.002	3.172
Cluster 2
sector = service	95.763	40.357	<0.001	12.028
efficiency = efficiency_	73.234	70.357	<0.001	10.591
sector = automotive	79.279	31.426	<0.001	6.952
size = small	58.398	80.714	<0.001	5.759
capital = Polish	54.125	96.071	<0.001	5.266
sector = bank_fin	67.961	25.000	<0.001	3.970
year = 1990–1999	62.000	22.143	0.010	2.564
Cluster 3
sector = industrial	92.157	67.143	<0.001	16.445
size = large	86.275	31.429	<0.001	9.679
size = medium	59.322	50.000	<0.001	9.076
capital = mixed	89.190	23.571	<0.001	8.488
year = 1989_and_before	73.333	23.571	<0.001	7.065
optimization = optimization_optimization	38.922	46.429	<0.001	4.759
capital = foreign	68.182	10.714	<0.001	4.264
flexibility = flexibility_flexibility	32.877	51.429	<0.001	3.331
efficiency = efficieny_efficiency	31.010	63.571	0.001	3.272
quality = quality_	33.108	35.000	0.011	2.540

Note: Cla/Mod = distribution of significant categories across clusters; Mod/Cla = distribution within-cluster.

presents the results of hierarchical clustering obtained from MCA. For clarity of interpretation, only categories with positive v-test values (indicating a stronger association of the given category with the cluster) and p-values less than 0.05 are shown for each cluster. The categories are presented in descending order of v-test values, starting with those most strongly associated with the cluster.

Additionally, the table includes the values of the Cla/Mod and Mod/Cla measures, which refer, respectively, to the distributions of significant categories within clusters and distributions within clusters themselves. For instance, it can be observed that 91.803% of IT industry enterprises belong to Cluster 1, and 82.353% of cases in Cluster 1 are IT enterprises. In this case, the high values of both indicators point to a strong association of the IT industry with Cluster 1.

Conversely, it can be noted that 27.364% of enterprises with Polish capital belong to Cluster 1, while 100% of cases in Cluster 1 are enterprises with Polish capital. This indicates that enterprises with Polish capital are significant in Cluster 1 but are not exclusively assigned to it, which may suggest their presence in other clusters (as is indeed the case with Cluster 2).

The first cluster consists of small enterprises in the IT sector, established between 2010 and 2019, with Polish capital. These enterprises more frequently than others identified the following benefits of Industry 5.0: increased production efficiency and improved quality of products/services. However, they did not observe benefits related to production cost optimization or flexibility in production adapted to changing market needs.

The second cluster primarily includes small enterprises in the service, automotive, and banking/financial sectors, established between 1990 and 1999, with Polish capital. These enterprises more frequently than others do not recognize the benefits associated with increased production efficiency.

The third cluster comprises large and medium-sized enterprises in the industrial sector with mixed capital, established in 1989 or earlier. These enterprises more often than others simultaneously identify benefits related to production cost optimization, flexibility in production adapted to changing market needs, and increased production efficiency. At the same time, they more frequently point to a lack of benefits related to improved quality of products/services.

The results presented in Table 3 confirm H1, indicating a faster pace of implementation of Industry 5.0 by companies from the IT and industrial sectors. Companies from other industries do not implement Industry 5.0 as often, because they do not associate any benefits with it.

Figure 9 presents the MCA results (projections of categories onto two main axes), which allowed for the identification of connections between challenges arising during the transformation toward Industry 5.0 and enterprise characteristics. Small IT sector enterprises established between 2010 and 2019 more frequently highlight the challenge of modernizing infrastructure and systems. Automotive sector enterprises recognize challenges associated with acquiring new skills and the costs of implementing new technologies. Service sector enterprises are clearly associated with not perceiving challenges such as the need for staff training, infrastructure, and systems modernization, and ensuring data and network security.

Based on the hierarchical clustering conducted using MCA, it can be concluded that all analyzed variables had a statistically significant impact on the composition of the clusters, with the enterprise sector having the greatest influence (

Table 4. Challenges during the transformation towards Industry 5.0: Description of the clusters by variables.

Variables	p-Value	df
Sector	<0.001	12
Size	<0.001	6
Capital	<0.001	6
Modernization	<0.001	3
Skills	<0.001	3
Costs	<0.001	3
Year	<0.001	12
Security	<0.001	3
Training	<0.001	3

Note: df = degrees of freedom.

).

In

Table 5. Challenges during the transformation towards Industry 5.0: Description of the clusters by categories.

Cluster	Cla/ Mod	Mod/ Cla	p-Value	v-Test
Cluster 1
sector = IT	81.148	81.818	<0.001	16.955
skills = skills_	40.067	98.347	<0.001	12.488
costs = costs_	33.518	100.000	<0.001	10.802
modernization = modernization_modernization	37.113	89.256	<0.001	9.677
size = small	30.749	98.347	<0.001	8.984
year = 2010–2019	36.000	59.504	<0.001	5.966
capital = Polish	24.346	100.000	<0.001	5.197
security = security_security	29.492	71.901	<0.001	4.742
training = training_	27.707	52.893	0.005	2.834
Cluster 2
modernization = modernization_	67.170	82.028	<0.001	13.328
sector = service	88.983	48.387	<0.001	12.729
security = security_	59.387	71.429	<0.001	9.337
size = small	49.612	88.479	<0.001	8.056
capital = Polish	42.455	97.235	<0.001	5.143
year = 1990–1999	59.000	27.189	<0.001	4.443
training = training_	47.186	50.230	<0.001	3.307
sector = bank_fin	51.456	24.424	0.005	2.823
costs = costs_	42.659	70.968	0.017	2.390
Cluster 3
costs = costs_costs	57.949	80.142	<0.001	12.878
skills = skills_skills	47.876	87.943	<0.001	11.802
training = training_training	39.385	90.780	<0.001	9.618
modernization = modernization_modernization	38.488	79.433	<0.001	7.626
sector = automotive	54.054	42.553	<0.001	7.331
size = medium	51.395	43.262	<0.001	7.023
security = security_security	37.288	78.014	<0.001	7.013
sector = industrial	52.941	38.298	<0.001	6.675
capital = Polish	26.962	95.035	0.008	2.635
Cluster 4
year = 1989_and_before	73.333	42.857	<0.001	9.673
size = large	60.784	40.260	<0.001	8.352
capital = foreign	90.909	25.974	<0.001	8.283
capital = mixed	70.270	33.766	<0.001	8.212
sector = industrial	38.235	50.649	<0.001	7.052
size = medium	28.814	44.156	<0.001	4.904
sector = bank_fin	28.155	37.662	<0.001	4.291
training = training_	19.481	58.442	0.001	3.194
skills = skills_	17.172	66.234	0.015	2.430
costs = costs_	16.066	75.325	0.038	2.080
modernization = modernization_	16.981	58.442	0.043	2.026

Note: Cla/Mod = distribution of significant categories across clusters; Mod/Cla = distribution within-cluster.

, the results of hierarchical clustering obtained from MCA are presented. The categories are listed in descending order of the v-test values, additionally providing the p-value and the values of the Cla/Mod and Mod/Cla indices.

Cluster 1 consists of small IT enterprises with Polish capital established between 2010 and 2019. These businesses, more often than others, recognize challenges related to the need for modernizing infrastructure and systems as well as ensuring data and network security, which supports H2 and H4 Additionally, they are more likely not to perceive challenges associated with acquiring new skills, the costs of implementing new technologies, and the necessity of staff training. Cluster 2 comprises small enterprises in the service and banking/financial sectors with Polish capital, established between 1990 and 1999. These companies, more often than others, do not perceive challenges related to modernizing infrastructure and systems, ensuring data and network security, staff training, or the costs of implementing new technologies, which supports H3 Cluster 3, including medium-sized enterprises in the automotive and industrial sectors with Polish capital. These businesses more frequently identify challenges such as the costs of implementing new technologies, acquiring new skills, the need for staff training, modernizing infrastructure and systems, and ensuring data and network security, which supports H2. The final cluster, Cluster 4, comprises large and medium-sized enterprises in the industrial and banking/financial sectors, established in 1989 or earlier, with foreign or mixed capital. These businesses, more often than others, do not recognize challenges related to staff training, acquiring new skills, the costs of implementing new technologies, or modernizing infrastructure and systems. However, it should be noted that the v-test values and Cla/Mod indices for the latter two challenges suggest that their association with Cluster 4 is not particularly strong.

5. Discussion

5.1. Implications

The article analyzed the extent to which the surveyed companies understand the concept of Industry 5.0 and whether they provide valuable and accurate information. The results indicate that only 25% of companies claim full knowledge of this model, while 66% have only partial knowledge, and 9% are entirely unfamiliar with the concept of Industry 5.0. These findings suggest that the majority of enterprises lack comprehensive awareness of the framework, which may limit the quality and accuracy of the provided data.

The analysis also revealed significant differences in the level of awareness and implementation of Industry 5.0 across sectors. Companies in the IT and industrial sectors show higher levels of advancement in adopting Industry 5.0 principles compared to those in the service and banking/financial sectors. IT companies most frequently highlight benefits such as increased production efficiency, while industrial companies and medium-sized enterprises with mixed capital focus more on cost optimization. In contrast, sectors such as banking/financial and services less often recognize the advantages of Industry 5.0 implementation, which explains their slower pace of transformation. Cost optimization is less emphasized in the service sector because its intangible outputs make cost-saving measures more difficult to quantify. Moreover, the sector often prioritizes customer satisfaction and personalized offerings, which can take precedence over direct cost reductions. Unlike the industrial sector, where optimization can be clearly measured through material or energy savings, service industries tend to focus on outcomes such as customer retention and brand reputation.

Despite these differences, the research shows that most enterprises are implementing Industry 5.0 principles gradually, highlighting the need for further support in education and technology, especially for businesses just beginning their transformation process. The article also emphasizes the importance of an approach that takes into account the specific characteristics of individual sectors and the unique needs of enterprises, which could contribute to a more effective adaptation of this modern economic model.

The conducted research confirms that the awareness of the Industry 5.0 concept among Polish enterprises is diverse. Only one in four companies reports a full understanding of this idea, while the majority of respondents acknowledge a fragmented understanding of it. The transition towards Industry 5.0 progresses at varying speeds, with gradual approaches prevailing, particularly in medium- and small-sized enterprises and the industrial sector. The study revealed that the main benefits of implementing this concept include improved product and service quality and enhanced production efficiency, particularly noticeable in technological industries. Identified challenges include the need to modernize infrastructure, ensure data security, and train personnel, with the costs of implementing new technologies posing a significant barrier for smaller companies.

The conducted research on the implementation of the Industry 5.0 concept in Polish enterprises revealed that awareness of this idea is limited, with many companies possessing only a fragmented understanding of the concept. Therefore, it is worth comparing these findings with other studies. Similar conclusions emerge from a report highlighting the need for strategies based on sustainable development while pointing out insufficient actions in this area [60,61]. Both analyses emphasize the necessity of increasing knowledge and awareness among Polish companies to fully exploit the potential of modern economic solutions.

Benefits such as improving the quality of products and services and enhancing production efficiency are also identified in the analyses as key elements of the transformation towards Industry 5.0 [62,63]. These solutions address the growing costs and sustainability requirements, making them attractive to Polish enterprises. This research aligns with findings that underline the necessity of investing in modern technologies and employee skill development to meet emerging challenges [64,65].

Findings related to challenges, such as modernizing infrastructure, ensuring data security, and the costs of technology implementation, are corroborated by analyses indicating that strategically addressing these elements can improve company competitiveness and support adaptation to rapidly changing market conditions [66,67]. Additionally, this research underscores the importance of a holistic approach to implementing the Industry 5.0 concept, considering technological, social, and environmental aspects. The results of our study also highlight the need for a holistic approach to transformation. It can be observed that the analysis of association rules and hierarchical clustering indicated that IT sector enterprises more frequently achieve production efficiency, while those with mixed capital and medium-sized businesses excel in cost optimization. Conversely, the automotive and industrial sectors face greater challenges in acquiring new skills and managing the costs of technology implementation. Thus, the findings highlight the importance of a holistic approach to transformation that also takes into account the specifics of the sector and the size of the enterprises. They also point to the need for greater educational and technological support for organizations at an early stage of implementing Industry 5.0. The results provide a foundation for further analysis and the development of implementation strategies to support companies in achieving goals aligned with a modern, sustainable economy based on collaboration between humans and technology.

Start-ups, established businesses, and legacy businesses each face distinct challenges and opportunities, requiring tailored managerial strategies to maximize their potential. The conducted research shows different managerial implications depending on the year of establishment of the company. Using a simplified criterion based on the number of years a company has been operating, we classify businesses as follows: companies established in 2020 or later (less than five years old) are considered start-ups; those established between 2010 and 2019 (5 to 15 years old) are categorized as established businesses; and companies founded in 2009 or earlier (operating for over 15 years) are classified as legacy businesses [68,69,70].

Start-ups (established in 2020 and after) demonstrate strong potential for adopting Industry 5.0 technologies, particularly in sectors like IT. Their agility and innovative nature allow them to quickly respond to market demands, but they often face challenges with outdated infrastructure and data security. Managers in start-ups should prioritize incremental investments in modern, scalable technologies and robust cybersecurity solutions while leveraging Polish capital through grants and partnerships. By addressing these issues early, start-ups can enhance their competitive edge and establish a foothold in dynamic sectors.

Established businesses (2010–2019) show a higher likelihood of implementing Industry 5.0 principles at a quicker or more structured pace, particularly in the IT and industrial sectors. These businesses often struggle with modernizing their infrastructure, making strategic investments in digital transformation critical. Managers should also focus on improving data security and leveraging Polish capital to sustain growth. Incremental change management, with high-impact, low-risk initiatives, can drive successful transformations while maintaining operational stability. Additionally, emphasizing product and service quality improvements aligns with the benefits of Industry 5.0.

Legacy businesses (2009 or earlier) excel in cost optimization and production flexibility, particularly in traditional sectors such as industrial and banking/finance. However, these firms often underrecognize challenges related to staff training and infrastructure modernization. Managers should address these gaps by implementing structured workforce development programs and gradually adopting Industry 5.0 technologies, leveraging their established infrastructure. Legacy businesses should also capitalize on their long-standing market trust to diversify offerings and expand into adjacent markets. Strengthening partnerships with investors and focusing on innovation in mature markets can further secure their competitive position.

In conclusion, while start-ups focus on agility and foundational growth, established businesses prioritize structured transformation, and legacy businesses leverage stability and market trust. Tailoring strategies to these unique profiles ensures each group can effectively address challenges and capitalize on opportunities in an evolving economic landscape.

It should be emphasized that in Poland the concept of Industry 5.0 is currently at an early stage of development. Although there is no formal public policy directly dedicated to promoting Industry 5.0, there are initiatives and strategies supporting the transformation of industry towards digitalization, sustainable development, and enhancing the role of humans in production processes. One of the key entities in this area is the Future Industry Platform Foundation (PPP), established by the Act of 17 January 2019. Its mission is to integrate the ecosystem of the future industry and support its development in Poland. The PPP development strategy for 2022–2025 includes issues related to digital, ecological, and energy transformations, as well as strengthening human capital, aligning with the principles of Industry 5.0 [71].

Moreover, interest in Industry 5.0 is growing in Poland among academic and industrial circles. An example of this is the “New Industry 4.0” conference in Katowice, where the future of industrial production in the context of Industry 5.0 was discussed [72].

It is also worth noting that the European Commission promotes the concept of Industry 5.0 as the next stage of industrial development, emphasizing a human-centered approach, sustainable development, and resilience. As a member of the European Union, Poland participates in these initiatives, which may influence the shaping of national policies in this regard in the future [73].

Based on the research, recommendations can be proposed to facilitate the implementation of the Industry 5.0 concept and enhance the efficiency of the transformation. First and foremost, enterprises should invest in raising awareness and knowledge of Industry 5.0 through training sessions, workshops, and industry conferences. It is also essential to develop employee competencies through tailored educational and training programs that enable them to acquire necessary skills in modern technologies and their practical application.

Companies should pay particular attention to modernizing technological infrastructure and implementing systems that ensure data security, which can be crucial in building trust among both employees and customers. Developing partnerships with the IT sector, which can provide technological solutions and support during the transformation process, is also recommended.

For small- and medium-sized enterprises, which often face financial constraints, it is advisable to explore opportunities for collaboration with other firms and utilize available funding support, such as technology development grants or innovation subsidies. Additionally, individual sectoral needs should be considered, and the pace of transformation should be tailored to organizational capabilities while monitoring the outcomes of implemented actions. Enterprises may also consider introducing more flexible work models to better adapt to changing market conditions and increase employee participation in decision-making processes. Promoting interdisciplinary collaboration and creating innovative project teams can further support the development and implementation of advanced technologies. At the strategic level, an integrated approach to Industry 5.0 implementation is recommended, encompassing technological, social, and environmental aspects. Developing long-term strategies that account for sustainable development can contribute to building a competitive advantage and enhancing the company’s attractiveness to potential partners and investors.

5.2. Theoretical Model

The essence of the considerations is the construction of an original theoretical model for implementing the assumptions of Industry 5.0 in enterprises. The proprietary model, presented in Figure 10, illustrates a comprehensive approach to integrating advanced technologies with various aspects of organizational operations, emphasizing the harmonious coexistence of technology, people, and the environment. The model includes the key stages of the transformation process and identifies the most critical elements necessary for the effective implementation of Industry 5.0.

In the initial stage, the model focuses on building awareness and understanding of the Industry 5.0 concept among decision-makers and employees. This process involves education, training, and informational activities aimed at increasing knowledge about the potential benefits and challenges of implementing modern technologies. The next step is the analysis of the enterprise’s needs and capabilities, including identifying areas requiring modernization and potential barriers to transformation. The model emphasizes the importance of technological and organizational diagnosis, enabling better alignment of the implementation strategy with the company’s specifics.

In the third stage, the planning and implementation of advanced technologies take place, including artificial intelligence, the Internet of Things, robotics, big data, and blockchain. This process requires simultaneous consideration of human-centric aspects such as work ergonomics, ethics, and data protection.

The model also highlights the importance of collaboration with external partners, including the IT sector, academic institutions, and non-governmental organizations, which can support enterprises in achieving their transformational goals. The final phase involves monitoring and evaluating the outcomes of the Industry 5.0 implementation. At this stage, it is crucial to measure the benefits achieved, such as increased production efficiency, improved product and service quality, cost optimization, and reduced environmental impact. The model also suggests continuous adaptation of strategies in response to changing market and technological conditions.

The entire concept is based on the premise that Industry 5.0 is not merely a technological transformation but also a cultural and social one, aimed at creating a sustainable, inclusive, and innovative work environment. This model serves as a practical tool to support enterprises in transitioning to a more sustainable and human-centric operational model.

5.3. Limitations

The conducted research encountered certain limitations due to several key factors that may influence the generalization of findings and the interpretation of data. Firstly, the use of a non-random sampling method limits the representativeness of the study group and prevents full generalization of the results to the entire population of enterprises in Poland. The sample focused on organizations from selected industries, such as IT, automotive, industrial, service, and banking/financial sectors, potentially overlooking specific challenges and benefits associated with Industry 5.0 in other economic sectors.

Another limitation involves data collection methods. Utilizing online surveys via the CAWI method may affect the quality of responses, especially in terms of question interpretation and respondent engagement. Additionally, while the questionnaire design was robust, it may not have captured all potential variables influencing the transformation toward Industry 5.0, limiting the comprehensiveness of the analysis.

A significant limitation is also the lack of long-term measurements that could account for changes in awareness and the level of enterprise engagement in the transformation process over time. The research focuses on a static snapshot at a given moment, making it difficult to assess the dynamics and evolution of transformation processes. Finally, despite using advanced analytical methods, such as association rules and hierarchical clustering, the results may be somewhat constrained by the adopted assumptions and statistical parameters, which should be considered in result interpretation.

6. Conclusions

The conducted research provides significant added value and new insights into the practical implementation of the Industry 5.0 concept in Polish enterprises. The analysis highlights tangible benefits, such as improved product and service quality and increased production efficiency, confirming the potential of this concept to enhance companies’ competitiveness in the market. Identifying common challenges, such as the need to modernize infrastructure, ensure data security, and manage the costs of technology implementation, enables a better understanding of the barriers slowing the pace of transformation, particularly in small and medium-sized enterprises.

New knowledge also arises from the application of analytical methods such as association rules and hierarchical clustering, which facilitated the identification of specific characteristics of enterprises most advanced in the process of implementing Industry 5.0. These findings provide insights into the relationships between company size, industry, capital structure, and the level of engagement in technological transformation. A particularly valuable contribution is the identification of sectors and types of enterprises that best leverage the potential of modern technologies and those requiring greater support.

The research also offers a fresh perspective on implementation strategies, emphasizing the importance of aligning the pace of transformation with organizational capabilities and industry-specific characteristics. These conclusions can be utilized by policymakers, innovation-supporting organizations, and enterprises themselves to develop more effective and sustainable action plans toward Industry 5.0.

As a result, the research contributes to the development of a new economic paradigm that integrates technological advancement with a human-centric approach and sustainable development.

Future research on implementing Industry 5.0 may include more detailed sectoral analyses focusing on the specific characteristics of individual industries and their adaptive capacities to new technologies. Conducting comparative studies between regions and countries to identify best practices and potential cultural or structural barriers would also be valuable. Further exploration of the social impacts of Industry 5.0, such as reducing inequalities, increasing inclusivity, and promoting sustainable development, is warranted.

Another area of interest could be analyzing interactions between advanced technologies and human capital, including identifying new competencies required in the future economy. Studies could also account for the long-term effects of implementing Industry 5.0, both in terms of economic efficiency and socio-ecological sustainability.

An important direction would involve developing research on integrating a human-centric approach with modern technologies, allowing for a more precise understanding of how technologies can support human well-being. Given the dynamically changing business environment, future studies could also explore the potential of international collaboration and knowledge-sharing networks for implementing this concept. These suggested research directions could contribute to building a more integrated and inclusive economy based on the synergy of people, technology, and the environment.