Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis

A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 1 QUALITY 4.0 KEY DIMENSIONS AND IMPLEMENTATION PROFILES IN ORGANIZATIONS OF EXCELLENCE: A CLUSTER ANALYSIS Calvo-Mora, Arturo1 ([email protected]) Alves, Helena2 ([email protected]) Villarejo-Ramos, Ángel F.3 ([email protected]s) 1,3Department of Business Administration and Marketing, University of Seville, Seville, Spain 2 Department of Management and Economics, NECE – Research Centre for Business Sciences, University of Beira Interior, Covilha, Portugal Abstract Quality has undergone various transformations and developments in recent decades due to technological, organizational, and social advances. Industry 4.0 (I4.0) currently involves the integration of new technologies, digitalization, and the massive use of data in manufacturing processes. This represents a paradigm shift in quality management. However, the meaning and fundamentals of Quality 4.0 (Q4.0) are still debated in the literature. In this context, the study's main objective is to identify the key dimensions and implementation profiles of Q4.0 in Spanish industrial and service organizations with excellence recognition systems. Different multivariate analysis techniques such as cluster analysis, contingency tables, and one-factor analysis of variance (ANOVA) are used as a methodology. The results identify three profiles of organizations (Advanced, In-development and Early-Stage) that apply the nine critical dimensions of Q4.0 identified with varying intensity. In addition, customer engagement, human resource management for Q4.0 and data for decision support are the dimensions that most help differentiate these organizations. Finally, differences were found in the degree of Q4.0 implementation according to size, sector and the degree of excellence achieved by the organization. Key words: Quality 4.0, Industry 4.0, dimensions, key factors, excellence, cluster analysis Managerial relevance statement. In a highly competitive environment, transformation and change are not options but necessities in all sectors. Industry 4.0 is revolutionizing organizational and quality management, urging organizations to invest in new technologies and digital training. However, such an investment is not enough to guarantee success in implementing Q4.0. Thus, the most developed organizations in Q4.0 stand out for the high degree of involvement of customers and human resources, A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 2 as well as for the constant support of data and information for decision-making. In addition, the Q4.0 implementation factors must form a socio-technical system in which the “traditional factors” of quality generate synergies with new technologies and digitalization. Finally, contrary to what might be expected, SMEs do not necessarily encounter more difficulties in implementing Q4.0 despite their financial constraints since they are much more flexible and are better prepared to initiate substantial organizational changes and transformations. 1. Introduction In today's environment, characterized by global competition, technological development, and innovation, organizations are forced to transform and reconfigure their management, manufacturing, and service delivery processes [25]. Industry 4.0 (I4.0), or the Fourth Industrial Revolution, is part of this transformation. I4.0 relies on new technologies, digitization, and the massive use of data to optimize manufacturing and service delivery processes, achieve greater flexibility, agility, and efficiency, and generate a value proposition for customers [58], [70]. I4.0 drives innovation and continuous improvement and creates a new way of managing and doing business [64]. However, the implementation of I4.0 also involves challenges. Thus, digitization requires a significant change in organizational culture, which may generate resistance and require appropriate change management strategies. Furthermore, protecting digital data and systems is essential to prevent cyber-attacks and leaks of confidential information or to ensure that all employees have the necessary skills and access to work in a digitized environment [68]. New technologies and the massive use of data can help improve quality through process control, realtime data collection or the application of analytics that help predict quality problems [32]. Digital tools also enable people to do their jobs better, faster, safer and at a lower cost [53]. Thus, quality management (QM) in the context of I4.0 or Quality 4.0 (Q4.0) is the integration of traditional QM practices and techniques with new technologies, resulting in an advanced collaborative environment in which management activities are driven by increased connectivity throughout the value chain, from A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 3 the supplier to the end customer [1]. For Sony et al. [59], Q4.0 is so much more than technology. It is a new method by which digital tools can be used so that organizations’ ability to consistently deliver high quality products can be improved. However, many organizations still need help with the quality of their processes, products and services, as shown by the high costs of poor quality in some industries regarding failures, repairs, warranty enforcement or after-sales services [60]. In this context, studies suggest that one of the most important impacts of I4.0 will be on the quality function of organizations [59]. Chiarini and Kumar [17] argue that Q4.0 redefines the approach to QM and decision-making in this business function. Incorporating the changes brought about by I4.0 into the traditional practices and methods of QM and improvement is a significant challenge for organizations. Along the same lines, de Souza et al. [21] point out that I4.0 represents a new paradigm for manufacturing and service delivery, in which traditional quality concepts must assimilate the changes and prepare for new challenges. For example, tasks previously performed by humans will be automated or supported by computers and even robots, reducing human involvement [36]. Zonnenshain and Kenett [70] go further, stating that the discipline of QM has stagnated in recent years, with few innovative models being proposed and quality professionals losing importance in organizational structures. Among the challenges posed by implementing Q4.0 in organizations are the need to invest in technological infrastructure and expertise, development of an organizational culture suited to the new industry, management of organizational change processes, and management of large amounts of data or cybersecurity [26], [40], [47]. More specifically, adapting to the Fourth Industrial Revolution requires considerable investment in advanced technological infrastructure, such as sensors, robots, and IoT platforms, and training specialized personnel in data science, AI, and cybersecurity. In addition, Q4.0 requires a cultural shift that fosters collaboration, innovation, and adaptability to change. Companies must effectively manage resistance to change, redefine roles, and train employees in new A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 4 skills. Big Data management and cybersecurity are crucial to protect sensitive information. Kushwaha and Talib [37] point out that addressing these challenges requires decisive support from top management, developing a strategic and collaborative approach involving key stakeholders, and education and training. New technologies, digitization, and the intensive use of data are revolutionizing QM in organizations, driving a transformation toward a more proactive, efficient, and customer-centric approach [17], [15]. Thus, the automation of repetitive manual tasks, automated data collection and analysis, and digital document management free up valuable time for quality teams, allowing them to focus on activities of greater strategic value [21], [64]. In addition, digitization enables real-time, granular capture and storage of quality data, from product design to customer experience. This provides complete visibility into quality performance across the value chain, facilitating problem identification and timely decisionmaking [26]. Similarly, analyzing large, quality data sets enable companies to identify trends, patterns, and relationships that were previously invisible. This enables organizations to make decisions based on solid data, proactively improving the quality of their products and services [65]. Also, analyzing data in real-time allows companies to anticipate quality problems before they occur, implementing preventive measures and reducing costs. Analyzing customer feedback through digital channels helps companies better understand their customers' needs and expectations, focusing their improvement efforts on the areas that matter most [64]. However, the organization must be aware of the problems or difficulties posed by new technologies, digitization, and the massive use of data. For example, automation and digitization can produce a technological dependency that can affect the ability to respond to system failures or cyberattacks, compromising the continuity of quality management operations. In addition, organizations must protect sensitive data and customer privacy [53], [27]. Information overload can make data analysis and interpretation difficult, requiring skilled personnel and advanced analysis tools. Finally, digitization of the quality function may generate resistance to change among staff, who may perceive A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 5 it as a threat to their traditional roles. Effective change management is essential to facilitate adaptation, foster the acquisition of new skills, and ensure successful digital transformation of the quality area [54], [20]. I4.0 is, therefore, an opportunity for the quality movement to become a leading force and a strategic element for organizations. However, it poses significant challenges, such as the need to unite the potential of I4.0 technologies with the potential of human knowledge, experience and skills [62], [36]. This implies a change in how QM is understood and implemented and the emergence of new competencies for quality professionals to cope with these profound changes [28], [65]. For Zulfiqar et al. [71], Ali and Johl [3] and Küpper et al. [36], technology is only one part of the quality transformation, which must also focus on people and a range of soft skills such as leadership, management commitment, change management, communication and teamwork. Successfully implementing Q4.0 requires optimizing both human and technical systems. Q4.0 is a concept at an early stage and further research is needed to better understand the phenomenon [17], [60], [70]. To date, studies in the area of Q4.0 have focused on: 1) identifying the motivations that drive organizations to implement Q4.0 [6], [60]; 2) estimating the benefits they expect to gain from implementation [6], [39]; 3) analyzing the challenges and barriers that can threaten the successful implementation of Q4.0 initiatives [4], [60]; 4) assessing and measuring the maturity of Q4.0 implementation [43], [46]; and 5) new technologies that can be useful for QM [39], [42], [50], [54]. However, no consolidated theory or universally accepted reference models guide the successful implementation of Q4.0 [71], [33], [60]. To this end, it is essential to identify the key readiness factors and dimensions that organizations need to consider when implementing Q4.0 [7], [62], [17], [60], [67]. Furthermore, these factors and dimensions may vary depending on the size of the organization [4], [7] and the sector in which it operates [7], [17], [60]. A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 6 In short, the literature review results suggest that empirical research is needed to understand the dimensions or factors of Q4.0, as it is an incipient study framework and not many organizations have developed projects and implemented Q4.0 [8]. Thus, the study's general objective is to identify the dimensions of Q4.0 and the characteristics of the most relevant organizations when differentiating between Q4.0 implementation profiles. To achieve this objective, an analysis is carried out in Spanish organizations recognized for excellence by the European Foundation for Quality Management (EFQM), i.e., organizations that seek excellence in management. This organization is committed to developing knowledge, innovation, learning, and capacity (EFQM, 2019) 1 . Therefore, they are risk-taking organizations, open to change and forwardlooking regarding emerging trends such as I4.0 and Q4.0 [28]. Moreover, as Murthy et al. [44] point out, EFQM is one of the best-known and most widely accepted business excellence models worldwide. The EFQM model aims to be a tool for management and transformation rather than for evaluation and improvement. It adds vision and forward-looking action, i.e., what is the organization doing to prepare for the future? In this way, transformation and change are not an option but necessary in all sectors and organizations. Considering organizations that implement the EFQM model as a reference in the study provides us with a vision of dynamic companies that seek transformation and excellence in their management models. The study identifies the critical dimensions for implementing Q4.0 in organizations and classifies them into three categories: social or soft skills, technological or hard skills, and strategic skills. Based on the critical dimensions identified, the profile of the organizations is obtained according to their development in implementing Q4.0 ("Advanced," "In development," and "Early stage"). The dimensions that most help to differentiate between groups of organizations are “Customer implications for Quality 4.0", followed by the factors "Human resource management for Quality 4.0" and "Datadriven decision making." Furthermore, there are differences in the degree of implementation of Q4.0 1 EFQM (2019) EFQM model. European Foundation for Quality Management, Brussels, Belgium. https://efqm.org/ A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 7 depending on the organization's size, sector, and level of excellence. All this contributes to advancing knowledge of the critical organizational and contextual dimensions for implementing Q4.0 and can guide organizations immersed in implementing and improving Q4.0-related projects. 2. Theoretical framework Organizations face the fourth industrial revolution (I4.0), which aims to transform industry into an intelligent manufacturing system using technologies [16]. I4.0 has come to respond to the growing demand for innovative solutions in operations, logistics, and services. It has enabled more customized products to be sourced and brought to the market, preserving mass production while meeting customer requirements [58]. Companies seek to be more competitive through flexibility of operations, zero inventory, efficient resource allocation, high responsiveness to market demand, and lower logistics and labor costs [50]. Another relevant aspect is that I4.0 allows analysis of a large amount of data collected throughout the organization's value chain in real time. These data provide helpful information for decision-making [52]. In addition, I4.0 has an essential impact at the organizational level and on people's motivation and working conditions [16]. Sony and Naik [58] indicate that in the context of I4.0, the most relevant challenges for organizations are those related to cybersecurity, data protection, training and qualification of workers, or the economic costs of the necessary digital transformation. For all these reasons, it is necessary to consider I4.0 as a strategic issue that workers and top management must be involved in and committed to. 2.1. Quality management in the fourth industrial revolution: Quality 4.0 Today's QM results from constant adaptation to the changing needs of organizations and the market. Its roots go back to the beginning of the 20th century when industrialization and mass production replaced handcrafted production. In this process, QM has been incorporating each era's most relevant management theories. At the same time, evidence of the impact of QM on business performance has emerged, along with specific theoretical proposals and academic paradigms. A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 8 In the first industrial revolution, QM focused on delivering defect-free end products to customers by inspecting and controlling defective products. However, the inefficiency of this system prompted the search for new approaches that considered not only products but also production processes [12]. Knowledge of reality is obtained by experimenting and seeking empirical evidence of the results of products and processes. The predominant paradigm is the empirical paradigm [66]. Since the middle of the 20th century, during the second industrial revolution, increased supply and competition have driven a more proactive and preventive approach to QM. Quality assurance and QM systems (ISO 9000 standards) emerged. This approach sought to identify and prevent risks in processes before they affected the quality of the final product and to meet customer needs and expectations [19]. In this context, the reference paradigm appears, which argues that quality has a subjective part, i.e., not everything can be measured based on objective and observable characteristics; it is essential to consider the customer’s perspective [66]. In the third industrial revolution, the term TQM was coined in the late 20th century, which recognizes that successful organizations go beyond customer satisfaction and financial results. They broaden their goals to include the well-being of other stakeholders, such as employees and society in general. In addition, employees' crucial role and commitment to achieving quality results at all levels is recognized [55]. TQM implies a high level of abstraction based on principles; what Van Kemenade and Hardjono [66] call a reflexive paradigm. Currently, in the fourth industrial revolution, digitization, new technologies, and the massive use of data have transformed QM, allowing organizations to be more productive and, at the same time, flexible. The concept of Q4.0 emerges, facilitating the design and manufacture of customized products efficiently and cost-effectively [70]. Here, the emergence paradigm becomes relevant. This considers quality a systemic, dynamic concept capable of creating flexible and innovative responses in a complex, dynamic, and uncertain context [66]. For Antony et al. [4], Q4.0 is a concept that has gained momentum in recent years and focuses on implementing digital solutions that enhance an organization’s ability to deliver consistently high- A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 9 quality goods and services to consumers. According to Chiarini and Kumar [17], Q4.0 redefines the approach to QM to cope with information technologies, increased connectivity and digitization throughout the supply chain, and the use of big data for decision-making. For Liu et al. [38], I4.0 offers excellent opportunities for QM to become a leading force in improving organizational performance, as it is difficult to achieve excellent quality levels with traditional QM methods in the current context. Mittal et al. [42] point out that Q4.0 integrates digital technologies with qualitative practices to drive continuous improvement, real-time monitoring and data-driven decision-making, leading to higher quality and customer satisfaction. For de Souza et al. [21], Q4.0 represents a new paradigm for manufacturing in which traditional concepts of quality must adapt to change and prepare for new challenges. For example, tasks previously performed by humans will be automated or assisted by computer equipment and even robots, reducing human involvement in these activities. More specifically, de Souza et al. [21] and Broday [12] point out that, despite new technologies imposed by I4.0, traditional QM methods will still need to be replaced or improved. Thus, I4.0 may have very relevant applications for inspection and quality control. For example, IoT devices and CPSs facilitate tracking products along the supply chain, from raw material to the final customer, allowing firms to identify the source of potential quality problems and take corrective actions accurately [5], [63]. In addition, the collection and analysis of massive data from various sources (sensors, machines, inspection histories, etc.) can identify trends, patterns, and relationships that were previously invisible, providing valuable information to improve quality control processes [59], [17]. Along these lines, Sader et al. [52] and Chiarini and Kumar [17] point out that new technologies integrate well with quality management and quality assurance systems. For example, they can extract and collect relevant real-time data for crucial management system requirements such as planning, process management, data-driven decision-making, or measuring customer satisfaction [69]. In addition, new technologies and digitalization help to reduce the formality or excessive documentation of quality assurance and A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 16 2.3. Contextual factors and Quality 4.0 implementation There are numerous studies in the literature on the effect of size and sector of activity on QM implementation, although the conclusions are contradictory. For example, Sila [57] finds no differences between the size of the organization and the use of different QM practices. However, Calvo-Mora et al. (2015) [13] find significant differences in that relationship. In the realm of I4.0, Ali and Johl [3] note that larger organizations are more prepared than smaller ones to take advantage of the potential of I4.0 technologies. Smaller entities need more resources, which translates into less ability to transform the potential of I4.0 into economic benefits and competitive advantages. Antony et al [7] find differences and similarities in Q4.0 implementation factors according to size. Thus, big data, improved productivity, and reliable data are the three main “pros” or positive aspects shared by large companies and SMEs. However, for SMEs, cost and time savings in the long term are not as crucial as for large companies. Moreover, these authors find no differences between SMEs and large companies regarding the barriers to implementing Q4.0: the high cost of implementation, lack of support from top management, lack of knowledge and financial resources, and organizational culture. Employee training did not seem to be a disadvantage for SMEs or large companies, since the transformation of organizations through Q4.0 would not be possible without intense employee training at all levels. Another context factor widely studied in the QM literature is the sector of activity. Ooi [48] found no significant differences in QM practices based on the sector. However, Criado and Calvo-Mora [18] discovered that industrial companies invest more in quality than service companies and make more intensive use of TQM practices such as benchmarking, failure reduction programs, or process improvement. In the context of Q4.0, Antony et al [7] identified higher accuracy, fewer errors, and better decision making as crucial Q4.0 factors for services, but less so for manufacturing firms. They also pinpointed high implementation costs, lack of financial resources, and lack of knowledge as the main barriers shared by manufacturing and service organizations. However, training is an aspect that poses a challenge for service companies, but not manufacturing ones. These shared challenges A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 17 underscore the need for a collective approach to overcome them. Finally, we have yet to identify studies that analyze the relationship between the level of organizational excellence and Q4.0 implementation factors. In sum, the study poses the following research questions (RQi): • RQ1. What are the key dimensions or drivers of Q4.0 adoption? • RQ2. Are all dimensions equally important in differentiating between advanced organizations in Q4.0 implementation? • RQ3. Do size, sector and level of excellence influence Q4.0 implementation? 3. Methodology 3.1 Sample and data collection The study population comprises Spanish organizations with an EFQM Recognition System or Seal of Excellence. In January 2022, there were 407 organizations with one of the current systems of recognition of excellence. These data have been obtained from the EFQM website (EFQM.org), specifically from the link https://shop.efqm.org/recognition-database. Once in the database, we can filter by sector, type of recognition or year of recognition. The questionnaire was sent to the total referenced population of 407 companies, and we obtained a response rate of over 26.29% (107 companies) of the total number of organizations with recognition of excellence, which is an acceptable response rate in research based on collecting information from companies. The answers were obtained between the end of 2022 and the end of 2023. This criterion was used because organizations involved in excellent management are committed to knowledge, innovation, development and improvement of capabilities as strategic drivers to achieve competitive advantages in the market [10]. Therefore, these organizations are aware of the challenges that the markets always pose, are willing to take risks and are proactive regarding new trends such as I4.0 and Q4.0 [41]. For Fonseca [28], the EFQM model is recognized as a comprehensive management framework for organizations, regardless of their size, sector, ownership or scope of activities. It drives change management, innovation, learning, continuous improvement and superior A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 18 stakeholder performance. Nenadál [45] and Murthy et al. [44] argue that the EFQM model can be a framework to base successful implementation of I4.0 and Q4.0. Data were collected by designing and sending a structured online questionnaire to the organizations’ top managers (CEOs) and positions related to quality management and improvement. Before final submission, the questionnaire was pilot tested to assess its reliability and validity [7]. A group of academics and practitioners with knowledge of I4.0 and Q4.0 participated in the pilot test. Once the final questionnaire was constructed, a first round of mailings was sent to organizations in March 2022 and a second round six months later in September 2022. The final questionnaires were received in March 2023. The questionnaire was answered by Quality Managers (53.3% of the sample) or by the CEO of the company. 55.1% of the companies are industrial and the rest are service companies. Less than half, 44.9% are large companies and the rest are SMEs. 64.4 % of the companies score highly on the EFQM (+400). The sample characteristics are shown in Table 3. Characteristics Frequency Percentage Respondent's position [1] CEO 50 46.7 [2] Quality managers 57 53.3 EFQM Recognition of Excellence (EFQM points) [3] 200+ 23 21.5 [4] 300+ 13 12.1 [5] 400+ 30 28 [6] 500+ 29 27.1 [7] 600+ 12 11.3 [8] 700+ 0 0.0 Main sector of activity [9] Industry 59 55.1 [10] Services 48 44.9 Organizational size [11] < 250 employees (SMEs) 59 55.1 [12] ≥ 250 employees (Large companies) 48 44.9 Table 3. Sample characteristics. 3.2 Measures and data analysis The final questionnaire was divided into three parts. The first part collected information about the respondent, type of recognition scheme, size of the organization, and the sector of activity. The second part asked about the management-related dimensions or factors of Q4.0. Finally, the respondent was asked about dimensions related to data analysis, analytical thinking and new technologies. A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 19 The scales used to measure the dimensions of Q4.0 were adapted from the studies by Antony et al. [7], Sureshchandar [63], Zulfiqar et al. [71] and Sony et al. [60] (Table 1, and Appendix I) to ensure content validity. A 1-7 Likert scale ('strongly disagree'-'strongly agree') was used to measure the variables representing the management-related dimensions of Q4.0. A 1-7 Likert scale ('very low' - 'very high') was also used to measure the dimensions related to data analysis, analytical thinking and new technologies. As can be seen in Table 4, the scales show internal consistency by obtaining Cronbach's alpha values above 0.7 in all cases. Dimensions Cronbach's alpha 1. Leadership and top management commitment (9 items) 0.922 2. Quality 4.0 vision and strategy (5 items) 0.925 3. Human resource management for Quality 4.0 (9 items) 0.912 4. Organizational culture towards Quality 4.0 (7 items) 0.923 5. Customer implication for Quality 4.0 (5 items) 0.917 6. Supplier implication for Quality 4.0 (6 items) 0.913 7. Data-driven decision making (5 items) 0.911 8. Analytical thinking and advanced analysis (4 items) 0.870 9. New technologies (5 items) 0.865 Table 4. Quality 4.0 dimensions measurements. Due to the general objective and the RQs set, cluster analysis is the most appropriate data analysis method. This analysis produces groups of organizations with similar characteristics, i.e. with a high degree of internal homogeneity and a high degree of external heterogeneity compared to other clusters. Among the available types of cluster analysis, we opt for a two-step or two-stage cluster analysis, because by comparing the values of a model selection criterion for different clustering solutions, the procedure can automatically determine the optimal number of groups, i.e. it offers an automatic selection of the number of clusters [30]. Furthermore, this method is particularly suitable when variables are measured on ordinal scales [31], as is the case for the Q4.0 dimensions. Based on the cluster analysis results, we use contingency tables to analyze whether there were significant differences in the clusters identified according to the size of the organization and the sector of activity. Finally, we carry out a one-factor analysis of variance (ANOVA) to compare the means of the different clusters obtained, given its suitability to detect significant differences between the means of independent groups. Before performing the ANOVA, the assumptions of homogeneity of variances were verified using the Levene test, and the normality of the errors using the Shapiro-Wilk test. Both A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 20 assumptions were satisfied, confirming the appropriateness of using ANOVA in this context to check whether there were significant differences in the clusters according to the level of excellence achieved by the organization. The statistical package used was SPSS Statistic V26. 4. Results 4.1. Cluster identification We use the factor loadings of the variables (dimensions of Q4.0) as scores to obtain the clusters. As Hair et al. [31] pointed out, the factor loadings capture the information of the variables better than the means of the items that make up each variable. As indicated above, the cluster analysis procedure was carried out in two stages (two-stage). It is an exploratory method that attempts to identify dataset groupings (or clusters) that would not otherwise be discernible. This procedure allows the use of both categorical and continuous variables. In addition, it makes an automatic selection of the optimal number of clusters [30]. The lowest Bayesian Information Criterion (BIC) classification is that of three clusters. Thus, considering the silhouette measures of cohesion and separation, the classification can be considered good (Figure 1). A result in the ‘good’ zone means that the data provide reasonable or strong evidence of cluster structure; a result in the ‘Enough’ zone means that the data provide fair evidence of this cluster structure; and a result in the ‘Poor’ zone reflects that the data do not provide significant evidence of cluster structure [49]. Specifically, the first group or cluster contains 59 organizations (55.1%), the second 25 (23.4%) and the third 23 (21.5%). Figure 1. Cluster analysis quality. 4.2. Predictor dimensions identification for cluster formation Figure 2 analyses the influence of each dimension of Q4.0 on ranking the three clusters identified. The most influential factor is 'customer impact on Q4.0', followed by 'human resource management for Q4.0', 'data-driven decision making', 'organizational culture for Q4.0', 'new technologies' and A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 21 'analytical thinking and advanced analysis'. These factors reach important values of at least 0.6 [49]. On the other hand, 'Supplier impact on Quality 4.0', 'Quality 4.0 vision and strategy' and 'Leadership and top management commitment' have the lowest discriminant power. Figure 2. Quality 4.0 dimensions classification according to their importance as predictors. 4.3. Cluster analysis by Quality 4.0 dimensions Figures 3, 4 and 5 show the mean factor loadings of the Q4.0 dimensions for the three clusters identified. The dimensions have been grouped into three figures according to their importance as predictors in ranking the organizations. In each graph, the further to the right of the graph the distribution of each dimension for each cluster, the higher the factor scores. Conversely, the scores are lower if the distribution is concentrated on the left. Thus, the organizations in Cluster 1 have higher scores than those in the other two clusters in all Q4.0 dimensions or factors, representing the most prepared and advanced organizations in implementing Q4.0 ('advanced' organizations). On the other hand, Cluster 3 organizations have the lowest scores, i.e. they are the least prepared and least advanced in implementing Q4.0 ("early stage" organizations). Finally, Cluster 2 organizations have intermediate scores, i.e. the distribution of factor scores is in the middle ('Developing' organizations). Figure 3 shows the loadings of the three factors that best discriminate behavior in the organizations of the Q4.0 dimensions (customer implication, human resource management, and data-driven decision making). We observe that in Cluster 1 (‘advanced’ organizations) these three factors are the 0.0 0.2 0.4 0.6 0.8 1.0 Customer implication for Quality 4.0 Human resource management for Quality 4.0 Data-driven decision making Organizacional culture towards Quality 4.0 New technologies Analytical thinking and advanced analysis Supplier implication for Quality 4.0 Quality 4.0 vision and strategy Leadership and top management commitment Less important More important A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 22 most important in predicting the companies’ behavior. The opposite is true for companies in Cluster 3 (‘early stage’ organizations) where the factor loadings are the lowest. In Figures 4 and 5 the situation is repeated for the two groups of factors (dimensions of Q4.0). The factors of ‘organizational culture for Quality 4.0’, ‘new technologies’ and ‘analytical thinking and advanced analysis’ in the second level, and those of ‘impact of suppliers on Quality 4.0’, ‘vision and strategy of Quality 4.0’ and ‘leadership and commitment of top management’ have the lowest discriminant power. Customer implication for Quality 4.0 Cluster 1 Cluster 2 Cluster 3 F r e q u e n c y Human resource management for Quality 4.0 F r e q u e n c y Data-driven decision making F r e q u e n c y Figure 3. Average factor loading scores of the highest discriminant power readiness factors. A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 23 Organizational culture towards Quality 4.0 Cluster 1 Cluster 2 Cluster 3 F r e q u e n c y New technologies F r e q u e n c y Analytical thinking and advanced analysis F r e q u e n c y Figure 4. Mean scores of factor loadings of readiness factors with intermediate discriminant power. Supplier implication for Quality 4.0 Cluster 1 Cluster 2 Cluster 3 F r e q u e n c y Quality 4.0 vision and strategy F r e q u e n c y Leadership and top management commitment F r e q u e n c y Figure 5. Average factor loading scores of the lowest discriminant power readiness factors. A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 24 4.4. Analysis of differences in organizational size Next, we use contingency tables to analyze whether there are significant differences in the clusters according to the organization’s size (nominal variable SME-large company). Table 5 shows statistically significant differences in the distribution of organizations by size between the different clusters (Chi-square: 11.139; Sig. 0.004*). Chi-square: 11.139 Sig. 0.004* SME Large company Cluster Count % of Cluster % of SME Count % of Cluster % of large company 1. Advanced (n = 59) 35 59.3% 59.3% 24 40.7% 50.0% 2. In development (n = 25) 18 72% 30.5% 7 28% 14.6% 3. Early stage (n = 23) 6 26.1% 10.2% 17 73.9% 35.4% Total (n = 107) 59 100% 48 100% * The chi-square statistic is significant at the 0.05 level. Table 5. Organizational size contingency table. In Clusters 1 (Advanced) and 2 (In development), SMEs represent a higher percentage than large companies. Large companies are in the majority in Cluster 3 (Early-stage). These results may be due to the characteristics of SMEs versus large firms. SMEs tend to be more agile and flexible when adopting new technologies since their decision-making processes are faster and they can adapt more easily to market changes. Large companies tend to have more complex organizational structures, making implementing large-scale changes and adopting new technologies difficult. In addition, many SMEs see innovation as necessary for survival and growth. As a result, they are more willing to invest in technologies and tools that allow them to improve their processes and products and thus compete. SMEs tend to have a closer relationship with their customers. This closeness facilitates the implementation of customized solutions and continuous process improvement. 4.5. Analysis of differences between organizational sectors We use contingency tables to analyze whether there are significant differences in the clusters according to the sector in which the organization carries out its main activity. It is also a nominal variable (industry-services). Table 6 shows statistically significant differences in the distribution of organizations by sector between the different clusters (Chi-square: 7.066; Sig. 0.029*). A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 25 Chi-square: 7.066 Sig. 0.029* Industry Services Cluster Count % of Cluster % of Industry Count % off Cluster % of Services 1. Advanced (n = 59) 39 66.1% 66.1% 20 33.9% 41.7% 2. In development (n = 25) 9 36.0% 15.3% 16 64.0% 33.3% 3. Early stage (n = 23) 11 47.8% 18.6% 12 52.2% 25.0% Total (n = 107) 59 100% 48 33.9% 100% * The chi-square statistic is significant at the 0.05 level. Table 6. Organization sector contingency table. In this case, we observe that the organizations belonging to the industrial sector are more concentrated among the advanced organizations (Cluster 1). On the other hand, organizations in the services sector are more evenly distributed between organizations in the development and early stage. The differences in the implementation of Q4.0 between the industrial and service sectors can be explained by a combination of factors. Production processes in industry tend to be more tangible and measurable, which facilitates the implementation of data-driven quality control systems and automation. In addition, the industrial sector has traditionally been a pioneer in adopting new technologies, facilitating the incorporation of Q4.0 solutions. Companies in the industrial sector tend to allocate greater resources to investment in technology and machinery, which facilitates the implementation of Q 4.0 solutions. Also, there are stricter regulations and quality standards in many industrial sectors, forcing companies to invest in more robust quality management systems. Finally, industrial companies tend to have a more production and efficiency-oriented culture, which can favor the adoption of Q4.0. 4.6. Analysis of differences in organizational excellence We perform a one-factor analysis of variance (ANOVA) to test for significant differences between the organization’s level of excellence, measured by the points it scores in the EFQM excellence recognition, and the three groups of organizations identified (Table 6). This test is a generalization of the equality of means for two independent samples. It is applied to test the equality of means of three or more independent and normally distributed populations [30]. Before testing for significant differences, Levene's test for homogeneity of variance must be performed. If, in Levene's test, the variances are not significantly different from each other (Sig. > 0.05), the assumption of homogeneity A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 32 We consider that the creation of funding programs to support companies, especially small and medium-sized enterprises (SMEs), in implementing Q4.0 technologies could accelerate adoption in sectors where resources for innovation are limited. Policies should consider the particularities of each sector, as Q4.0 adoption can vary significantly across sectors. In addition, policies that incentivize the creation of Q4.0 training and professional development programs can serve to close the skills and training gap in human resources, facilitating effective implementation of these technologies. To this end, collaboration among academic institutions, government agencies and industry should be encouraged, to develop curricula and training programs that prepare the next generation of Q4.0 professionals. 6. Implications, conclusions and limitations 6.1. Theoretical implications This research contributes to the existing literature in four ways, within the framework of theory on TQM, innovation, knowledge management, and organizational learning. First, reference models for implementing Q4.0 need to include dimensions related to people and their behaviors (soft), technology (hard) and strategy. This is what Antony et al. [5] call a socio-technical system, in which Q4.0 must be aligned with organizational strategies and there must be a symbiotic relationship between people and technology. In this regard, Q4.0 models should incorporate strategies for continuous skills development in employees, not only in the technical domain but also in soft skills such as communication, collaboration, and adaptability. Leaders should actively promote the Q4.0 culture, inspiring and motivating employees to adopt the necessary changes. Implementation models should also encourage employee participation in the design of Q4.0 solutions, leveraging their knowledge and experience. Finally, it is essential to continuously monitor and evaluate the impact of Q4.0 on organizational performance, tangible results, and employee well-being. Secondly, three dimensions are key in organizations that can be considered benchmarks in implementing Q4.0: Customer Impact, People Management and Data-Driven Decision Making. In addition, the dimensions of Leadership and Top Management Commitment, Quality 4.0 Vision and A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 33 Strategy, and Supplier Impact on Quality have been identified as fundamental or essential aspects that every organization should consider when embarking on Q4.0 implementation projects. Therefore, Q4.0 models should incorporate strategies to collect and analyze customer interaction data to customize products and services, anticipate their needs, and exceed their expectations. They should also encourage the creation of solid and transparent relationships with suppliers based on trust and information sharing to ensure quality throughout the supply chain. In addition, models should promote the creation of training and development programs to prepare employees for the challenges of Q4.0, fostering technical and soft skills. There must be a focus on creating an organizational culture in which data is used systematically to make informed decisions at all company levels. Leaders must be agents of change, communicating the Q4.0 vision, inspiring employees, and removing barriers to adopting new technologies and processes. Thirdly, SMEs in the industrial sector are the most prepared and developed to implement Q4.0. Although SMEs have more limited access to human, technological or financial resources, they are more flexible and receptive to change than large companies, where complexity and bureaucracy can hinder rapid and effective implementation of the organizational changes needed to implement Q4.0 successfully. In addition, industrial organizations develop more measurable and tangible processes and are more likely to incorporate new technologies into their day-to-day operations. This theoretical implication challenges the conventional notion that large companies, because they have access to more resources, are better positioned to lead Q4.0 adoption. Thus, Q4.0 implementation models should emphasize the importance of agility and adaptability as critical factors for success rather than focusing solely on financial and technological resources. In addition, networks and collaboration platforms between companies should be promoted, facilitating the exchange of knowledge, experiences, and resources to accelerate Q4.0 adoption. Fourthly, in the absence of a consolidated reference model accepted by the scientific community, the EFQM Model of Excellence (in its 2020 version) can serve as a basis and impetus for implementing Q4.0. It is a model committed to change, innovation, creativity, disruptive thinking, and learning, key A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 34 aspects that support the organization’s digital transformation. The EFQM model emphasizes the importance of establishing a clear vision and a solid strategy and anticipating future trends and needs. In addition, it puts the customer at the center of the strategy, promoting understanding of their needs and expectations and creating long-term sustainable value. It also recognizes the importance of attracting, developing, and retaining talent and fostering a culture of learning and continuous improvement. Finally, the EFQM model promotes a leadership style that inspires and empowers employees, encouraging collaboration and teamwork. These are essential to drive digital transformation and the adoption of Q4.0. 6.2. Practical implications Based on the study's findings, the following suggestions can help managers prepare for the challenges of implementing Q4.0. First, in today's highly competitive environment, managers must be aware that transformation and change are not an option but a necessity in all sectors and organizations. In this context, I4.0 is revolutionizing organizations’ management in general and quality management in particular. It is an incentive for organizations to invest in new technologies, their employees' training and digital skills, and adopt new business models that emphasize differentiation through innovation, product customization, and improving quality and excellence. For this reason, and without models for implementing Q4.0, organizations could look to excellent models such as EFQM. The transformation to Q4.0 requires solid, committed leadership capable of driving investment in new technologies, fostering training in digital skills, and promoting an organizational culture open to innovation and change. In the digital era, customer satisfaction is paramount. Organizations must adopt a customer-centric approach, involving customers in processes and using data analytics to understand and anticipate their needs. Q4.0 also requires an adaptable and skilled workforce, so organizations must invest in digital skills development, foster knowledge management, and promote a culture of continuous learning. Finally, it is critical to remember that Q4.0 is not a stand-alone project but an integral component of business strategy. Managers must be able to integrate social, technological, and strategic aspects into a coherent and effective management system. A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 35 However, we should focus on sectoral recommendations in the manufacturing sector. Adopting technologies such as robotics, artificial intelligence, and the Internet of Things can optimize production, improve product quality, and personalize supply. Managers must be prepared to invest in these technologies and adapt their production processes. On the other hand, in the services sector, Q4.0 offers opportunities to improve the customer experience, personalize services, and optimize operational efficiency. To achieve these objectives, managers should explore using technologies such as data analytics, process automation, and artificial intelligence. Q4.0 can improve patient care, optimize clinical information management, and facilitate evidence-based decision-making in healthcare. Managers should consider adopting technologies such as telemedicine, artificial intelligence, and data analytics to transform the delivery of healthcare services. Finally, in the education sector, Q4.0 can enhance the learning experience, personalize education, and optimize the management of educational resources. Managers should explore using technologies such as online learning, virtual reality, and data analytics to transform education. Finally, implementing Q4.0 presents challenges and opportunities for organizations in all sectors. Managers who take a proactive approach, invest in technology and talent, and foster a culture of innovation will be better prepared to meet the challenges and seize the opportunities presented by this new era of quality management. 6.3. Conclusions The study advances knowledge of the critical organizational and contextual dimensions of implementing Q4.0. It can guide organizations immersed in the implementation and improvement of Q4.0-related projects. In addition, it identifies what practices are being developed by organizations that are most advanced in implementing Q4.0. These should be followed by organizations that intend to make further progress in developing Q4.0 practices. More specifically, following the research questions set, nine critical dimensions for the implementation of Q4.0 (RQ1) have been identified, including social, technological, and strategic factors of quality management. The dimensions must be implemented systemically to achieve A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 36 success. Three groups of organizations have been identified as “Advanced,” “In development,” and “Early-stage”, differing in the degree of development of the key dimensions of Q4.0. The most developed organizations (Advanced) stand out in the degree of customer involvement, human resource management for Q4.0, and the use of data to support decisions. In contrast, the study presents a relevant novelty in literature: it can identify a series of basic dimensions that do not help differentiate between organizations but must always be present to start any Q4.0 project that aims to be successful. These basic dimensions are the leadership and commitment of top management, considering Q4.0 as a strategic issue, and involving suppliers (RQ2). Finally, the study identified that the profile of organizations with the highest degree of implementation of Q4.0 is small and medium-sized companies operating in the industrial sector and with systems of recognition of excellence (RQ3). It may seem strange that SMEs have a higher degree of Q4.0 implementation than large companies. The inherent differences between SMEs and large corporations could explain these findings. SMEs, being more agile and flexible, can adopt new technologies more easily, thanks to their faster decisionmaking and ability to adapt to market changes. In contrast, large companies with more complex organizational structures need help in implementing large-scale changes and adopting new technologies. In addition, many SMEs see innovation as crucial to their survival and growth, which drives them to invest in technologies that improve their processes and products, enabling them to compete effectively. The close relationship that SMEs often have with their customers facilitates the implementation of customized solutions and continuous improvement of their processes. 6.4. Limitations and future research The first limitation is that the sample was drawn from organizations located in a specific geographical area that have systems for recognizing business excellence. By focusing exclusively on companies recognized for excellence, we are losing information about strategies and practices developed by other organizations. It is understood that excellent organizations have a solid foundation for implementing Q4.0, so we also lose information about problems and obstacles faced by other A. Calvo-Mora, H. Alves and Á. F. Villarejo-Ramos, "Quality 4.0 Key Dimensions and Implementation Profiles in Organizations of Excellence: A Cluster Analysis," in IEEE Transactions on Engineering Management, vol. 72, pp. 527-545, 2025, doi: 10.1109/TEM.2025.3531645. 37 organizations. For all these reasons, future research should extend the sample to all types of organizations to be able to make a more in-depth comparative analysis. Secondly, SMEs are a very heterogeneous group in terms of size, ownership, activity, knowledge and technology intensity and workforce skills. Therefore, the implementation of Q4.0 in SMEs requires further analysis. It might be interesting to analyze the state of Q4.0 implementation in different types of industries rather than in an aggregated way, as was done here. In this line, it would also be relevant to provide information on operational practices, strategic orientations, or specific Q4.0 initiatives within each type of cluster identified, as well as information on effective strategies and technologies in leading organizations and concrete examples that illustrate successful implementation of Q4.0. 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