Quality Management in the Context of Industry 4.0: Necessary and Sufficient Readiness Factors

1 DOCTORAL THESIS Quality Management in the Context of Industry 4.0: Necessary and Sufficient Readiness Factors PhD Candidate: João Pedro Galrito Bento Teles Rico Supervisors: PhD. Arturo Calvo de Mora Schmidt Universidad de Sevilla PhD. Cayetano Medina Molina Centro Universitario San Isidorio, Sevilla Seville, July 2024 2 3 GRATTITUDE This work is the result of my effort and involvement, dedication, commitment and belief. But beyond me, it also reflects the involvement of a set of people and entities. In this way, and before expressing particular thanks and recognition, a sincere thank you to everyone in advance! This moment represents the end of a cycle that began at the beginning of the covid-19 pandemic and at a time when the world closed in on itself, in the hope that by combating a virus that threatened people's lives, it could be reborn into a life better, where human and social values could prevail for a new reality in the world. It is therefore a very special and important moment of personal assessment in my life, in which I am faced with the responsibility of taking on new skills and allowing myself to apply them in various contexts. It is therefore time to take another step on this journey called Life. The doctorate also represents a period of life where it is possible to grow personally and also to experience self-knowledge and discovery. The development of certain skills that we think do not exist, can "emerge" by participating in a program of this type, managing to change us into better people. I’ll keep and carry all this Knowledge acquired into my life, making me equally grateful for it. "The time" to embark on a program of this type in the current phase of my life was not an easy decision, as it involved facing challenges that in a "more certain time" (if there is a right time to make this trip?) would come to pass. would make your participation smoother. However, belief and some family support were the factors responsible for facing the inherent risks, fears and uncertainties. This is proof that when we truly want and believe and put feeling into what we do, in addition to the honest result, certain positive things happen in our lives. Therefore, once again practicing gratitude is fundamental for our personal growth. To my Professor, Advisor and Director (PhD) Arturo Calvo de Mora Schmidt, who is mainly responsible for me being here and now presenting this work, for his patience, unconditional support and guidance, which from the first moment of my arrival at the University of Seville and "embark on this unknown journey" called Doctorate, was always present, having in the most difficult moments a word of understanding, motivation and strength towards the challenges presented along this path, even with the barriers of physical presence and language. To my advisor, Prof. (PhD) Cayetano Medina Molina, who, despite being along on this "journey" only at a later stage, also played a fundamental role, both in the process of preparing the scientific article and in his preponderant role in the methodological process, and data analysis of this research work. To him, an equal thank you for his involvement and support. To Prof. (PhD) Helena Alves, who despite not having directly participated in the preparation of this dissertation, also played an important role in terms of participation in the published scientific article, 4 being a reference in this scientific area, her analyzes and comments were of primary importance . To her, a sincere thank you. To the University of Seville, in particular the Faculty of Economic and Business Sciences (FCEYE), in the body of Professors and collaborators of the "Program Doctorate Gestion Estratégica y Negocios Internacionales (PDGENI)" and in the person of its Director, to all a sincere and Thank you very much for my acceptance and entry into this program and the opportunity to carry out my studies outside my country (Portugal), because without you it would not have been possible to achieve this long-desired goal of mine. To my colleagues and friends who I had the opportunity to meet throughout this journey, an equal word of thanks, as they are also part of the context of this journey. To all other people and entities who, although not mentioned individually, may have had a direct and/or indirect participation and influence in the preparation and contribution of this work. To all of them an equal and sincere thanks. To my little living family, to my dear mother! She is and represents my strength to live. Your unchanging values are very present in me and in my conduct of life, and they also constitute an inspiration and example. Your strength, motivation and support throughout this journey were extremely unconditional and were always present in your daily life. To my dear and late father, who, despite being absent from the physical plane, will certainly applaud, follow and witness this special moment in my life wherever he is. He is and will always be present in my memory and heart. To my maternal grandparents, who despite also being absent from the physical plane are and will always be present in my memory, as an inexhaustible source of energy and inspiration for life. To them a thank you the size of the universe. “I feel born every moment into the eternal newness of the world.” By Fernando Pessoa, Portuguese poet and writer. 5 CONTENT INDEX 1. ABSTRACT ................................................................................................................................. 11 2. INTRODUCTION ....................................................................................................................... 13 2.1 Background of the Research .............................................................................................. 13 2.2 Relevance and Topic Justification ..................................................................................... 15 2.2.1 Research Gap ............................................................................................................... 17 2.2.2 Research Questions (RQ) ............................................................................................ 18 2.3 Contributions and Research Objectives ............................................................................ 18 2.4 Methodological Issues ......................................................................................................... 20 2.5 Thesis Structure ................................................................................................................... 23 3. THEORETICAL FRAMEWORK............................................................................................. 25 3.1 Introduction and Objectives ............................................................................................... 25 3.2 Industry 4.0 .......................................................................................................................... 25 3.2.1 The Industrial Revolutions ......................................................................................... 25 3.2.2 Origin, Concept, Objectives, and challenges ............................................................. 27 3.2.3 New Emerging and Enabling Technologies for Industry 4.0 ................................... 38 3.2.3.1 Cyber Physical System (CPS) ............................................................................. 43 3.2.3.2 Internet of Things (IoT) ...................................................................................... 43 3.2.3.3 Internet of Services (IoS) .................................................................................... 44 3.2.3.4 Industrial Internet of Things (IIoT) ................................................................... 44 3.2.3.5 Cloud based manufacturing (cloud computing) and smart manufacturing (blockchain) .......................................................................................................................... 44 3.2.3.6 Industrial information integration ..................................................................... 45 3.2.3.7 Radio frequency identification (RFID) .............................................................. 45 3.2.3.8 Enterprise resource planning (ERP) .................................................................. 45 3.2.3.9 Social product development ................................................................................ 46 3.2.3.10 Business Process Management (BPM) ............................................................. 47 3.2.4 Key factors to implement Industry 4.0 in organizations .......................................... 47 3.2.4.1 Readiness of organizational strategy ................................................................. 51 3.2.4.2 Level of digitization of the organization ............................................................ 52 3.2.4.3 Extent of digitization of supply chain ................................................................ 53 3.2.4.4 Smart product and services ................................................................................ 54 3.2.4.5 Employee adaptability with Industry 4.0 .......................................................... 55 3.2.4.6 Top management involvement and commitment .............................................. 55 3.2.5 Framework to usher towards industry 4.0 implementation .................................... 57 6 3.2.6 Challenges of Industry 4.0 .......................................................................................... 60 3.2.7 Impacts of Industry 4.0 ............................................................................................... 63 3.2.7.1 Industry ................................................................................................................ 63 3.2.7.2 Products and services .......................................................................................... 64 3.2.7.3 Business models and market ............................................................................... 64 3.2.7.4 Economy ............................................................................................................... 64 3.2.7.5 Work Environment .............................................................................................. 64 3.2.7.6 Skills Development .............................................................................................. 64 3.3 Quality 4.0 ............................................................................................................................ 65 3.3.1 Quality 4.0: Concept and Approaches ....................................................................... 66 3.3.1.1 Concept of Quality 4.0 ......................................................................................... 68 3.3.1.2 Approaches to Quality 4.0................................................................................... 69 3.4 Systematic Literature Review (SLR) ................................................................................. 71 3.4.1 Formulation of the problem to solve .......................................................................... 75 3.4.2 Definition of inclusion criteria and exclusion of articles .......................................... 78 3.4.3 Article search and selection ........................................................................................ 79 3.4.4 Search Results .............................................................................................................. 79 3.4.5 Analysis of the SLR quantitative research ................................................................ 83 3.4.5.1 Analys and comments to the aims Quality 4.0 studies. .................................... 93 3.4.5.2 Instruments, Quantitative analysis types and Activities sectors ..................... 95 3.4.6 Conclusions and limitations ........................................................................................ 97 3.4.6.1 Motivations and barriers for Quality 4.0 implementation ............................... 98 3.4.6.2 Quality 4.0 Challenges....................................................................................... 100 3.4.6.3 Limitations ......................................................................................................... 102 4. RESEARCH MODEL AND HYPOTHESIS .......................................................................... 103 4.1 Leadership and top management commitment .................................................................. 105 4.2 Q4.0 vision and strategy ..................................................................................................... 106 4.3 Human resource management for Q4.0 ............................................................................ 106 4.4 Organisational culture towards Q4.0 ................................................................................. 107 4.5 Customer readiness ............................................................................................................ 107 4.6 Supplier readiness .............................................................................................................. 108 5. METHODOLOGY .................................................................................................................... 109 5.1 Introduction and Objectives ............................................................................................. 109 5.2 Target sample and total population ................................................................................. 112 5.3 Data Description & Data Collection ................................................................................ 114 5.4 Measurements .................................................................................................................... 114 7 5.5 Data Analysis Process ....................................................................................................... 115 6. RESULTS ................................................................................................................................... 119 6.1 Introduction and objectives .............................................................................................. 119 6.2 PLS-SEM (Sufficient Causality) ...................................................................................... 119 6.2.1 Measurement Model ................................................................................................... 119 6.2.2 Structural Model ......................................................................................................... 123 6.2.3 Predictive Model ......................................................................................................... 124 6.3 Identifying Necessary Conditions with Necessary Conditions Analysis (NCA) ........... 125 6.4 Identifying sufficient conditions with Generalised Analytic Induction (GAI) ............. 129 6.5 Identifying the necessary conditions with Qualitative Comparative Analysis (QCA) 131 6.6 Identifying the sufficient conditions with Qualitative Comparative Analysis (QCA) . 132 7. DISCUSSIONS AND IMPLICATIONS.................................................................................. 139 7.1 Discussion of the Results ................................................................................................... 139 7.2 Theoretical and Management Implications .................................................................... 144 8. CONCLUSIONS, LIMITATIONS AND FUTURE LINES REASERCH ........................... 147 9. BIBLIOGRAPHIC REFERENCES ........................................................................................ 149 10. ANNEXS ................................................................................................................................ 175 10.1 Annex I. Details of Industry 4.0 technologies .................................................................. 175 10.2 Annex II: Questionnaire on an organisation's readiness for the implementation of Quality 4.0 ...................................................................................................................................... 180 10.3 Annex III: “QUALITY 4.0 SOCIAL AND STRATEGIC READINESS FACTORS: SUFFICIENT AND NECESSARY CONDITION ANALYSIS” .............................................. 182 8 INDEX OF TABLES AND FIGURES Table 1. Industry 4.0 Characteristics and Capabilities. Source: Sader et al., 2019 ............................... 29 Table 2. Industry 4.0 components conceptualization. Source: adapted from Ynzunza-Cortés et al. (2017) .................................................................................................................................................... 32 Table 3. Perspectives under Industry 4.0. Source: adapted from Ynzunza-Cortés et al. (2017) ........... 32 Table 4. Implications of Industry 4.0. Source: adapted from Ynzunza-Cortés et al. (2017) ................. 32 Table 5. Elements of the SWOT analysis (Calabrese et al., 2021). ....................................................... 37 Table 6. New emerging technologies for Industry 4.0 by authors. Source: Owner ............................... 38 Table 7. Definitions of Quality 4.0 ........................................................................................................ 69 Table 8. Quality 4.0 Literature Review. Source: owner ........................................................................ 92 Table 9. Aims of the analysed research. Source: Ower. ........................................................................ 94 Table 10. Type of instruments for obtaining the data used. Source: Owner. ......................................... 96 Table 11. Types of quantitative analyses used. Source: Owner. ............................................................ 96 Table 12. Types of sectors of activity analysed. Source: Owner. .......................................................... 97 Table 13. Sample description characteristics. Source: Owner. ............................................................ 114 Table 14. Measurement model results. Source: Owner. ...................................................................... 122 Table 15. Measurement model: discriminant validity Source: Owner. ............................................... 123 Table 16. Bootstrap confidence interval through critical value t and intervals. Source: Owner. ........ 124 Table 17. PLSpredict assessment of indicators. Source: Owner. ......................................................... 125 Table 18. NCA ceiling accuracy and effect sizes. Source: Owner. ...................................................... 127 Table 19. Bottleneck table (percentages). Source: Owner. .................................................................. 129 Table 20. Models for high Quality 4.0 readiness. Source: Owner. ...................................................... 130 Table 21. Models with denial of a high degree of Quality 4.0 readiness. Source: Owner. .................. 130 Table 22. Analysis of necessary conditions RQ/ ~RQ. Source: Owner. .............................................. 132 Table 23. RQ parsimonious solution. Source: Owner. ........................................................................ 133 Table 24. ~RQ parsimonious solution. Source: Owner. ...................................................................... 134 Table 25. Intermediate solution RQ. Source: Owner. .......................................................................... 135 Table 26. Intermediate solution ~RQ. Source: Owner. ........................................................................ 135 Table 27. Identification of core and peripheral conditions of RQ. Source: Owner. ............................ 136 Table 28. Identification of core and peripheral conditions of ~RQ. Source: Owner. .......................... 137 Table 29. Situations relevant to the interpretation of the results. Source: Owner. .............................. 140 Table 30. Situations relevant to the interpretation of the results from QCA. Source: Owner. ............ 141 Table 31. Details of Industry 4.0 technologies. Source: adapted from Dohale, V, et al. (2022) .......... 179 9 Figure 1. Key enabling technologies and tools for Industry 4.0............................................................ 40 Figure 2. Industry 4.0 maturity Stages. Source: adapted from Sütőová et al. (2023). ........................... 48 Figure 3. Smart Manufacturing Framework. Source: adapted from Dohale et al. (2022). .................... 58 Figure 4. Systematic literature review (SLRs). Source: adapted from Behara (2019). ......................... 72 Figure 5. Essential ingredients and readiness factors for the successful implementation of Quality 4.0. Source: Owner. ...................................................................................................................................... 78 Figure 6. Article Selection Process. Source: Owner. ............................................................................. 79 Figure 7. Documents selected by year. Source: Scopus (2024). ............................................................ 80 Figure 8. Articles selection per year by source. Source: Scopus (2024). .............................................. 80 Figure 9. Documents by author. Source: Scopus (2024). ...................................................................... 82 Figure 10. Documents by affiliation. Source: Scopus (2024). .............................................................. 82 Figure 11. Documents by country or territory. Source: Scopus (2024). ................................................ 82 Figure 12. Documents by type. Source: Scopus (2024). ....................................................................... 83 Figure 13. Documents by subject area. Source: Scopus (2024). ........................................................... 83 Figure 14. Motivations and barriers to the implementation of Quality 4.0. Source: Adapted from Sony et al. (2021). .......................................................................................................................................... 99 Figure 15. Research model and hypotheses. Source: Own elaboration. .............................................. 105 Figure 16. Structural model results. Source: Owner. ........................................................................... 124 Figure 17. NCA ceiling lines. Source: Owner. .................................................................................... 128 Figure 18. Graphical representation of parsimonious solutions. Source: Owner. ............................... 134 Figure 19. Graphical representation of intermediate solutions. Source: Owner. ................................. 136 16 Quality-oriented perspective, proposing a transition plan to Q4.0, which can be controlled, measured and managed, thus minimizing implementation and change costs. For Joshi et al. (2014), Quality 4.0 (Q 4.0) refers to the supervision of quality in the contemporary scenario of Industry 4.0 (I 4.0), where there is the fusion of cutting-edge I 4.0 technologies, which foresees Q 4.0 helping organisations to achieve extraordinary organisational performance, operational excellence and constant improvement through quality management systems, tools and methodologies, signifying a global change in technology and manufacturing processes. According to Kenett et al. (2020) in the era of Industry 4.0, quality focuses on data and information. Illés et al. (2017) highlight how the technologies of Industry 4.0 allow collecting data and information on quality until now unknown. The challenge for companies is to identify where, how, what data to collect, as well as how to work with a large amount of unstructured data (Big data). For Sony et al. (2020), the important thing is that technologies allow data to be collected throughout the entire life cycle of the product, which facilitates the understanding of the needs of customers and other stakeholders, helping companies to achieve a better balance between design, cost and added value of the product. Sussintly and according to Deloitte (2017), “Industry 4.0 signifies the promise of a new Industrial Revolution: In short, Industry 4.0 is ushering in a digital reality that may alter the rules of production, operations, workforce even society.” For Küpper et al. (2019), technology is just one more piece in the quality transformation, which must also focus on people and soft skills such as change management, communication or teamwork. This new competitive environment implies a significant change in strategy, structure and processes, so the organisation must be ready (Sony and Naik, 2020). By that way, Haffar et al. (2020) point out that successfully implementing Q4.0 requires addressing people, strategic and cultural issues. Furthermore, although companies recognise that Q4.0 can contribute to creating value for the organisation and its customers, few of them have defined a deliberate strategy and initiated projects that lay the foundations for Q4.0 to succeed (Sony et al., 2020; Sureshchandar, 2023). This is because it is difficult for organisations to plan and assess whether they are ready to implement Q4.0 (Sony et al., 2020; Antony et al., 2023b). According to Chiarini (2020), Sony et al. (2021) and Sony et al. (2020), as mentioned before, during this research it will be aboard the readiness factors to implement Quality 4.0. Also, what it will be possible to observe according to Durana et al. (2019) and Haffar et al.(2020), the keys to Industry 4.0 and Quality 4.0 are in automation, digitization, and data management, although the main barriers are not technological, but leadership, cultural and resistance to change. Therefore, it is crucial to identify which readiness factors an organisation should consider before implementing and applying Q4.0-related projects (Antony et al., 2023b). As Kumar et al. (2022) indicate, quality management in I4.0 is still nascent, and researchers are trying to identify critical success 17 factors. While most of this research focuses on the factors that will produce specific outcomes, those that facilitate outcomes are equally important for decision-making (Van der Valk et al., 2016). In this sense, no research in the Q4.0 literature addresses the identification of must-have and should-have conditions. In this study, we fill this gap. To do so, we reference the readiness factors for Q4.0 proposed by Antony et al. (2023b). As it will opportunity to observe during this thesis, and according to Chiarini (2020) and Sony et al. (2020 and 2021), the necessary synergy between technological factors (hard factors), social-human- cultural (soft factors) and strategic (strategic factors) for the successful implementation of Quality 4.0 is shown in which a series of "essential or key ingredients" are identified for the Q 4.0 implementation as (1) Big data Use, (2) Improved prescriptive analytics, (3) Vertical, horizontal, and end-to-end integration, (4) Search for strategic advantage, (5) Leadership, (6) Training, knowledge, and awareness, (7) Organizational culture, (8) Top management support, (9) Customer orientation, (10) Orientation to suppliers, as well as its Motivations and barriers hence arising. 2.2.1 Research Gap After the “state of art” relevance and topic justification analysis, we proceed to esblish and focus in what are the research gaps on the research topic. According this, it is important to mentioned again the fourth industrial revolution, called “Smart Industry” or “Industry 4.0” that the Organizations are facing now, and that according to Sánchez-Franco et al. (2023), a revolution that generates a new way of organising production based on digitalisation and the massive use of information. This increases efficiency and organisational flexibility. Also, it is important to mentiond according to Sader et al. (2022), the Quality 4.0 term results from integrating I4.0 into quality management practices, and it can also be considered as the fourth phase in the evolution of quality. That said, and with the lack of non-abundance of specialised literature on this subject in study, since according to the evolution of the TQM concept, for Zonnenshain and Kenett (2020), the discipline of quality and its management has stagnated in recent years, since few innovative models are proposed for quality management, and quality professionals have lost importance in organisational structures (Zonnenshain and Kenett, 2020; Prashar, 2023). It is from here that possible research gaps are identified and future research opportunities open up. Thus, the main gap identified for this research centres on the following: According to Antony et al. (2023b), no research in the Q4.0 literature addresses the identification of “must-have” and “should-have” conditions related to which factors an organisation should consider before implementing and applying Q4.0-related projects. In this study, we fill that gap. To do so, we reference the readiness factors for Q4.0, since, and according to Kumar et al. (2022), quality management in I4.0 is still nascent, and researchers are trying to identify critical success factors. With this study, other “gaps” will be identified and proposed in addition in a future researchs. In that point, we can consult the chapter 6 of this research, particularly in the area of “future lines research”. 18 There will be propose new future studies related to possible moderating effects of contextual factors of Q 4.0 implementation process. 2.2.2 Research Questions (RQ) Following on from the previous point, questions arise that need to be answered because they are what will give meaning to this research. In this way, as in the previous point and once the "state of art" regarding the relevance and justification of this topic under investigation has been established, it is important to systematise and "put down on paper" what the author's concerns are and what will shape the course of this study, in accordance with the authors already mentioned, namely:  RQ1: Are companies, particularly Spanish companies, "ripe" to receive or implement a quality management system in an Industry 4.0 context?  RQ2: What are the critical (necessary and sufficient) factors to be observed in a company so that it is ready to receive or implement a quality management system in an Industry 4.0 context (Antony et al., 2023b)? According to Durana et al. (2019) and Haffar et al.(2020), it is possible to ask the following question:  RQ3: Is it only technological issues that an organisation needs to consider when implementing a quality management system in an Industry 4.0 context, or is it also cultural issues, leadership and resistance to change? Having asked ourselves these questions, we must now continue with this study, establishing its objectives and defining methodologies in order to be able to answer them. This can be seen in the methodology chapters and in the results of this doctoral thesis through the justification of the hypotheses raised and the theoretical models created in chapter 2 ("theoretical framework") to answer these research questions. 2.3 Contributions and Research Objectives “It consists in formulating in an explicit, clear, comprehensible, and operational way the difficulty with which we face and to which we intend to respond (Sousa and Baptista, 2011:18)”. The final definition of the research theme appears gradually as the author collected data from referential authors from this knowledge area, particularly Industry 4.0, Quality 4.0 and finally from Readiness factors. The author had the vision to try to identify potential issues that could be responsible for the inappropriate implementation of "TQM" (Total Quality Management) in organizations. These issues could cause a delay in the transition to Quality 4.0 in an Industry 4.0 environment. The central question of the thesis is how to provide an appropriate solution to this problem. 19 This study is expected to serve as a benchmark and guide for organizations interested in implementing a Quality 4.0 system. It is hoped that this research will also enable them to obtain an EFQM excellence seal. These issues serve as the starting point for defining the research objectives. The author also pretends to help students of finance, management, and marketing courses, entrepreneurs, consultants, and companies from the "Industry 4.0, Quality 4.0, and Readiness factors" universe to learn its main concepts. These concepts are related to the information system and knowledge management area, and their application in their respective fields. It pretends to identify the main reasons behind the ineffective implementation of Quality 4.0 systems in an Industry 4.0 context. By understanding these reasons, the target audience can make necessary changes, adaptations, or introductions in their organizations to achieve success. This study also will investigate the significance of Industry 4.0 (I 4.0), Quality (Q 4.0), and Readiness Factors at theoretical, business, and social levels. This study will discuss the factors that organizations need to consider before implementing Quality 4.0 (Q4.0) related projects. According to Kumar et al. (2022), quality management in Industry 4.0 (I4.0) is still in its early stages, and researchers are working to identify the critical success factors. While most research focuses on the factors that produce specific outcomes, it is equally important to consider the factors that facilitate those outcomes when making decisions (Van der Valk et al., 2016). However, no research in the Q4.0 literature has addressed the identification of "must-have" and "should-have" conditions. To fill this gap, this study refers to the readiness factors proposed by Antony et al. (2023b) for Quality 4.0, this being its general objective. In a specific way, this study has two main objectives. Firstly, to identify the factors that are sufficient for an organization to implement Q4.0, which is a specific type of causality where a particular factor produces an outcome, but it may not be necessary for the outcome to occur (Dul et al., 2020; Richter et al., 2020). The Partial Least Squares Structural Equation Modelling (PLS-SEM) and GAI techniques are used in this first objective as mentioned before. Secondly, to identify the factors that are necessary for an organization to prepare for Q4.0, in which a necessary causality implies that the result will not occur without the condition, but its presence does not guarantee the result. These types of factors represent barriers or obstacles that others cannot compensate for (Dul et al., 2020; Richter et al., 2020). The Necessary Condition Analysis (NCA) technique is used here. To complete both analyses, two techniques based on set theory will be applied to identify both the sufficient conditions that are linked to the first specific objective and the identification of necessary conditions linked to the second objective. While Qualitative Comparative Analysis (QCA) identifies both types of conditions, Generalized Analytic Induction (GAI) identifies sufficient conditions. 20 2.4 Methodological Issues As we don't want to develop this topic too much at this stage of the research, we do have the opportunity to take a brief look at methodological issues, which will be developed further in the appropriate chapters. Thus, as will be explained later, taking into account the proposed research objectives, this empirical study opted, on the one hand, to use the "PLS-SEM" technique to estimate the sufficient conditions (Hair et al. 2019a), as well as the use of "GAI - Generalised Analytic Induction" to identify the combinations of conditions that explain the existence of a given result (Ragin, 2023), and, on the other hand, the use of the "necessary conditions analysis - NCA" technique (Dul et al., 2023) to identify the necessary factors, 2023) to identify the factors needed to prepare an organisation for Q 4.0. According to Bokrantz and Dul (2023), to develop and test embedded necessity theories, an empirical study must use a conventional analysis method, such as structural equation modelling (SEM), along with a method that analyses necessity. This is necessary because traditional quantitative methods, such as SEM, do not cover the logic of necessity and are, therefore, unable to analyse the existence of necessary conditions (Bokrantz and Dul, 2023; Dul, 2023). The behaviour of a necessary cause and a sufficient cause are fundamentally different. Necessary causes direct attention to overcoming potential barriers (Dul, 2024). Current perspectives on causality focus on deterministic causality (in QCA) or probabilistic sufficiency (in regression-based approaches). Both are rooted in Hume's first definition of causality (Dul, 2024). A necessary condition is a factor that must be, an enabler that makes the existence of the outcome possible. A certain level of the cause must be present for the result to be present. If the required level of the cause is not present, the outcome will not exist and the necessary condition becomes a bottleneck for the outcome. No other outcome can compensate for the absence of the required level of cause (Dul, 2024). Necessary conditions analysis (NCA) may be used to solve this issue. NCA ensures a fit between theory and method when the theory includes necessary relationships between factors and outcomes, and these relationships need to be empirically evaluated (Richter and Hauff, 2022; Linder et al., 2023). In the case where the theory in use posits the existence of necessary conditions, the use of NCA can ensure theorymethod fit (Dul, 2024). Therefore, to ensure a theory-method fit, it's important to use both SEM and NCA in an empirical study that attempts to develop and test embedded necessity theories (Dul, 2021; Dul, 2023). For Dul (2023) multiple factors contribute to the success of complex organizational change. However, only a few of them are essential. Therefore, according to Sony and Naik (2020), it is crucial to identify the "must-have" factors that should be controlled for the successful implementation of new processes and projects in organizations. This is important to understand how to successfully implement certain management practices (Richter et al., 2020; Richter and Hauff, 2022). According to Sony et al. (2021) and Antony et al. (2023a), before implementing Q4.0, organizations must assess the readiness of their 21 determinants. This means identifying the necessary conditions that must be present for successful implementation. However, necessary conditions are usually not fully recognized in the theoretical approach or empirical analysis (Richter and Hauff, 2022). Ignoring necessary conditions in research can result in incomplete findings and recommendations. Therefore, it is important to identify and consider all necessary conditions when conducting research in this field (Richter et al., 2020; Bokrantz and Dul, 2023). The findings of this study will enhance the knowledge in the field of Q4.0. In particular, it will provide a better understanding of the factors that indicate the readiness of an organization to implement Q4.0 and assess its level of development or maturity. The study can also help managers and quality managers distinguish between necessary, sufficient, and necessary and sufficient readiness factors. This will undoubtedly guide and support the successful implementation of Q4.0 in organizations and optimize decision-making in this area. To achieve our goal, we have developed a theoretical framework that covers the essential aspects of Industry 4.0 and Quality 4.0. This framework also establishes hypotheses regarding the factors required for the preparation of Quality 4.0. We describe the methodology of our work, which differentiates between sufficient conditions such as PLS-SEM and GAI, and necessary conditions such as NCA. Also, we will present the results of our analysis, followed by a discussion of the implications, conclusions, and limitations of our study. We also propose future research directions. To make all this possible, it was considered as study population, the Spanish organisations with recognition of excellence awarded by the European Foundation for Quality Management (EFQM). According to the information available on the website of the Club Excelencia en Gestión (EFQM partner in Spain), at the beginning of 2022, there were 407 organisations of this type. The choice of this target population is motivated by the fact that this type of organisation is committed to knowledge, innovation, development and improvement of capabilities as strategic drivers to achieve competitive advantages in the markets. A questionnaire was used to collect data and was sent to directors, managers and quality-related positions via email. The first questionnaires were sent out in March 2022, while the last ones were received in January 2023. A total of 107 valid questionnaires were obtained, giving a response rate of 26.29%, which is considered an acceptable rate for online surveys, as per Easterby-Smith et al. (2012), and was therefore considered our sample base. Talking about the measurement issues in an introduction style, and based on the research conducted by Antony et al. (2023b), a team of experts and scholars, who possess knowledge of Q4.0, participated in a pilot test. The main objective of this pilot test was to evaluate the reliability and validity of the measurement tool. Through this test, the group was able to ensure the clarity of the questions, identify any missing or redundant questions, and determine the time required to complete the questionnaire. 22 Although most of the group's feedback was positive, some changes were made based on their suggestions. The most significant changes included the grouping of some Q4.0 readiness factors, eliminating redundant questions or items, and rephrasing some confusing questions. Our final questionnaire was divided into three blocks. The first block requested information on contextual variables. The second block comprised questions about Q4.0 readiness factors, adapted from the work of Antony et al. (2023b). A 7-point Likert scale was used to measure the items, where (1) indicated "Strongly disagree", (4) indicated "Neither agree nor disagree", and (7) indicated "Strongly agree". Finally, the third block asked respondents to rate the degree of preparation of their organization for implementing Q4.0. The scale used was a 7-point Likert-type scale, where (1) indicated "Very low level", (4) indicated "Medium level", and (7) indicated "Very high level". Organisational Q4.0 readiness and factors were modelled as reflective variables or constructs (Mode A) in PLS-SEM, following Hair et al. (2021). Doing an introduction of the analytical approach, the PLS-SEM technique will be used in this study to estimate causal-predictive relationships (sufficient conditions). In particular, PLS-SEM will be useful in three situations: (1) when conducting exploratory studies (Hair et al., 2019b); (2) when working with a small sample size (Richter et al., 2016); and (3) when testing a theoretical framework in terms of prediction (Hair et al., 2019b). The last point is especially important since this question cannot be answered using covariance-based structural equation modelling (CB-SEM) (Rigdon, 2012). Also, to conduct this study, we used the Smart PLS 4 programme (Ringle et al., 2022). The goal of the additive logic used in PLS-SEM is to explain an outcome by using various determinants. According to Richter and Hauff (2022), Bokrantz and Dul (2023), and Dul (2023), these different factors can balance each other out and are sufficient but not necessary to change the outcome. Richter et al. (2020) suggest that only when the must-have variables are at the required level, the should-have factors uncovered by the PLS-SEM analysis can be revealed. For Richter et al. (2020) and Dul (2023), NCA highlights necessary circumstances that cannot be compensated for in their absence, as opposed to examining the average correlations between dependent and independent variables. Combining Partial Least Squares (PLS) and Necessary Condition Analysis (NCA) is a beneficial strategy to generate complementary findings in research, which allows theoretical development. According to Richter et al. (2020) and Richter et al. (2022), it helps to test theoretical arguments regarding both must-have factors and should-have criteria. When applying NCA and PLS-SEM together in the same work, it is recommended to use the construct scores generated by PLS-SEM. This is because the generation of these scores takes into account the context of the structural model that represents the underlying theory and corresponds to the idea of verifying the needs in the context of PLS (Richter et al., 2020). 23 Analytical induction is a qualitative analysis method that was developed in sociology and also used here in our research. It aims to identify the shared conditions among a group of cases that lead to a specific outcome. Therefore, it is a valid methodology for assessing antecedent conditions that are linked to qualitative outcomes (Ragin, 2023). The analysis conducted through analytical induction seeks to identify shared antecedent conditions. These conditions can be interpreted as prescriptions that describe and interpret the phenomenon under investigation, along with combinations of causally relevant conditions (Acocella, 2023; Ragin, 2023). Analytical induction was initially introduced as a case-based generalization strategy (Acocella, 2023; Wieringa & Danura, 2015). Additionally, it combines theory testing with theory generation (Wilson et al., 2002). Ragin (2023) develops GAI, which focuses on the antecedent conditions widely shared by cases that lead to a particular result, making it possible to have cases that do not present the conditions. 2.5 Thesis Structure This thesis is structured in the following manner. The present introductory chapter is followed by Chapter 2, where the main theoretical considerations (“Theoretical Framework”) will be developed as the main central research marc of this thesis. Also, it is at this point that will be approach “the state of art” of the proposed theme. In methodology, chapter 3, is the part that will aboard the main issues related to the research process for this study. Also, it is the part where we formulate the path necessary to acquire the “Scientific Knowledge” and answer the research questions necessary to achieve the research objectives. For that, the author will propose to use three different techniques, tools or even concepts necessary to achieve it. According to the approach, this theme will be analysed using the Partial Least Squares Structural Equation Modelling (PLS-SEM) technique to estimate sufficient conditions (Hair et al. 2019a). Across QCA will identify the conditions that are necessary to prepare organisations for Q4.0, as well as the combinations of conditions that are sufficient for the presence of Q4.0 readiness. Likewise, through GAI, the combinations of conditions that explain the existence of a given outcome will be identified (Ragin, 2023), and the Necessary Condition Analysis (NCA) technique (Dul et al., 2023) to help to identify the factors necessary in degree to prepare an organisation for Q4.0. NCA complements QCA in identifying the degree of the necessary condition that must be satisfied to reach a certain level of a variable (Medina-Molina & Rey-Moreno, 2024). The chapters 4 and 5, the author will present the research results consolidation and after will make its discussion and implications for the Management. Chapter 6 is the final part of this thesis before the bibliographic references presentation and the attachments, where it will be possible to take some considerations about the research objectives achievement, as well as the limitations of this research. It 24 will be possible to observe there, what could be the future lines of research trends. Different theoretical and practical contributions are highlighted as well. The chapters 7 and 8 as it mentioned before will be dedicated to the bibliographic references and attachments, respectively. 25 3. THEORETICAL FRAMEWORK 3.1 Introduction and Objectives Organisations are facing the fourth industrial revolution, called "Smart Industry" or "Industry 4.0". A revolution that generates a new way of organising production based on digitalisation and the massive use of information. This increases efficiency and organisational flexibility (Sánchez-Franco et al., 2023). According to Yedigis and Weinback (2005), the development of a theoretical perspective is considered a process as well as a product. Once the research problem has been posed, the next step consists of theoretically grounding the study. To this end, two main stages will be carried out: the "discussion" of the research problem already presented in the previous chapter and the presentation of the theoretical approaches of the various authors needed to support and understand the research problem presented. This section aims to discuss the context of the dissertation topic's research problem, reviewing the approaches and foundations of knowledge about Industry 4.0, Quality 4.0, and the factors for preparing to implement a Quality 4.0 system, as well as its Sistematic Literature Review (SLR). Next, the theoretical approaches needed to understand the main constructs and hypotheses presented will be reviewed, and the approaches chosen by the author to underpin this research will also be presented: what factors are necessary to prepare for the implementation of a Quality 4.0 system, and what factors are sufficient for these organisations to also be prepared to implement them. 3.2 Industry 4.0 3.2.1 The Industrial Revolutions The first industrial revolution (late 18th century) was one of the greatest changes in humanity from a social, economic, and technological point of view. Artisanal, labour-intensive production processes became mechanical, in which machines became increasingly important (Joyanes, 2018). At this time, for example, the first mechanical loom was designed in 1784 and patented in 1785 by Edmund Cartwright. The economy changed from agricultural and livestock farming to industrial. All these changes were possible thanks to the appearance of the steam engine, which made it possible to increase production using coal as the main raw material (Roig, 2017). Also, the railway allowed the transport of people and goods in large dimensions. All this provoked an exodus of people from the countryside to the cities and the emergence of a new social class, the workers. In the second industrial revolution (mid-19th century), technological, scientific, social and economic changes and innovations accelerated. New sources of energy such as oil, gas or electricity and new materials for manufacturing such as steel and some petroleum derivatives appeared. New transport systems such as aeroplanes, automobiles and new steam engines began to be used. Major inventions 32 Table 2. Industry 4.0 components conceptualization. Source: adapted from Ynzunza-Cortés et al. (2017) Industry 4.0 S mart factories S mart cities Smart Products Intelligent services Integrated technology artifacts Sensors, microchips, autonomous systems, cyber - physical systems, autonomous machines Features intelligence, flexibility, connectivity, security, reliability, traceability, mobility, collaboration, sociability, sustainability Principles of design Integration, interoperability, virtualization, decentralization, real - time capabilities, service orientation, versatility Support architecture Internet of Things (IoT), Radio Frequency Identification (RFID), Industrial Networks, High Performance Computing (HPC), mobile computing, cloud, and Internet of services (Infrastructure (IaaS), technology platforms (PaaS) and software (SaaS) as a service), big data and advanced analytics Benefits Demand-oriented production, more efficient use of resources, productivity, cost reduction, shorter product development cycle, greater competitiveness, process optimization, autonomy in decision-making and more integrated supply chains. Table 3. Perspectives under Industry 4.0. Source: adapted from Ynzunza-Cortés et al. (2017) Industry 1. Fully digitized and interconnected production facilities 2. Flexible, autonomous, and sustainable manufacturing 3. Robots that assist and interact with humans 4. Machines that learn, self - manage, and maintain 5. Intelligent products, aware of the environment and that provide information 6. management and analysis of large volumes of data in real time 7. Focus on innovation and value - added activities Business 1. Changes in the type of products from physical to digital connected 2. Higher levels of integration of the organization and the environment 3. Networks that create value 4. Infrastructures and shared services in the cloud 5. Smart and global supply chains 6. Business models based on cloud services, logistics and distribution Job 1. New collaboration schemes and social infrastructures 2. Other forms of man - machine interaction 3. highly specialized job profiles 4. More complex work processes 5. More flexible work 6. Assisted and technology - dependent work environments 7. Requirements for the management of digital technologies, robots, programming, and analytics as transversal competences. Table 4. Implications of Industry 4.0. Source: adapted from Ynzunza-Cortés et al. (2017) According to Vaidya et al. (2018), the evolution and Industry 4.0 trends is related with the next step: Industry 5.0, has been introduced to the research areas which are considered as next industrial revolution, 33 but it is more systematic transformation that includes impact on civil society, governance and structures, and human identity in addition to solely economic/manufacturing ramifications. For Asif (2020), and according to the World Economic Forum (2018), Industry 4.0 will accompany the following developments: First, AI (Artificial Intelligence) can help to make intelligent decisions and reduce material use, decrease energy consumption, clean energy, and solve problems where traditional wisdom did not work. Second, increased automation will bring in higher productivity, efficiencies, and reduce stress on human and environmental resources. Third, Industry 4.0 will enable sharing across devices and facilities, such as offices, homes, rides, that can reduce consumption-related waste and positively impact the life cycle of products. Fourth, Industry 4.0 allows greater decentralization and decision making by relevant people. Another point of view is giving us by Shan et al. (2020). For them, Industry 4.0 has emerged as a promising technology framework for integrating and extending manufacturing processes at intraorganizational and interorganizational levels. From a system perspective, Industry 4.0 is the application of advanced technologies to manufacturing and service (Weber, 2016). For Wee et al. (2015), industry 4.0 digitizes manufacturing with cyber–physical systems by connecting humans and robots as well as sharing and analysing information. It involves manufacturing and process of value adding and knowledge management in product and services (Haleem and Javaid, 2019). Industry 4.0 offers new technologies to facilitate interconnection and computerization in manufacturing. For Brettel et al. (2014), industry 4.0 proposes to decentralize production through shared facilities in the integrated global industrial system for on-demand manufacturing to increase efficiency, customization, innovation, profitability, performance, and better management by enabling technologies, methods, and tools (Albers et al, 2016). For instance, 3D printing can help to make better design and reduce emissions originating from logistics. Finally, Industry 4.0 will generate real-time data, that is readily available to all decision-makers allowing more collaboration, and improved quality of decision making. Related to the industry 4.0 objectives, for Montero (2020), these are mentioned below:  Competitiveness improvement: Due to the use of advanced technologies in the analysis of data from machines and processes, it is possible to optimize manufacturing with regard to energy consumption, management of material resources, monitoring and maintenance of machines, reducing time in performing tasks , it is process simulation, it is the creation of higher quality products, to avoid manufacturing errors and failures, to increase the training of employees and the organization of human resources;  Machines and Processes Monitoring: Empower with sensors, the machines that intervene more and more in the manufacturing processes, and that provide information about their 34 operation. This information is analysed in real time, and is used to monitor the state of these machines.;  Manufacturing Processes Simulation: The digital twin concept, where the manufacturing processes simulation insert, consist in a digital clone of the real object with the physical assets, processes, spaces, and systems issues of the "true object", that we can use to different kinds of objectives, particularly to prevent risk situations and take some of preventive measures.  Manufacture with zero defects: Is one of the principal’s goals of the industry 4.0 and consist in the occur supervision process in all productive process, since the design, fundiction, forge, mechanics, and assembly steps. If the first one’s steps of the production will have any kind of errors, will affect all the next production steps until the end.  Custom manufacturing or massive customization: Consist to the possibility to produce single and different kind of products for each user. for that is necessary new kind of robots and machines working fast and quickly change its way of work. It's the definition of the flexible and adaptable manufacturing, that brings to the new robots and machines connectivity to decide by quick and focused the control central instructions.  Energy Consumption Optimization: Consist in some of advanced technological tools to monitor the energetic consumption and offer information in real time about that. for this is used the data base analysis.;  Offer new services to our customers. Servitisation: Servitisation is a term and a paradigm associated to Industry 4.0, and it's based on the possibility to offer addicted services to the clients due to the sold product information. This is only possible due to the commercialized product, supply data related to its operation, and that data arrive directly to the manufacturer in real time due to the sensors and connectivity installed on the devices.;  Increase the Product Information’s. Traceability: Traceability consist of the client exhaustive information demand about the manufacturing processes, used materials on the product creation, or the conditions to which it has been exposed during storage or transfer.  Distribution and Location of Products improvement. Logistics 4.0: The logistic 4.0 or smart logistic is an Industry 4.0 related term. Is the emerge technologies Consumption, storage, products distribution whose produced we use in our fabrication. the logistic 4.0 principal goals are to manage time series of answer in most short time, adopt smart logistic models, adapt to the channel’s transformation, use the information to anticipate the client’s needs, and manage end to end traceability.  workers training enrichment: According to virtual reality is possible for workers improve his abilities, just like recreate and build process and scenarios to allow the workers learn how the machines work. This type of technology allows the workers training dangerous situations without consequences to the machine and worker and without risks between them. Also, is a 35 great way for the companies save money without production break, even without feedstock consumption. The Gamification is another way for workers improve his abilities, across video games access.  Avoid work accidents in factory: This is only possible with the new machines and robots, particularly with “cobots” or collaborative robots. This new type of machine came with sensors, artificial vision, and other technologies capable to reduce the production velocity, preventing plant accidents. The virtual reality, Internet of Things (IoT) devices, artificial vision and analytic visual and drones are also technologies who could prevent factories accidents.  working conditions improve: The factories’ future will be smart and intelligent factories with temperature control systems, noise, or emissions. Also, the new technologies will have an impact in an organizatives issues and people motivation. The "LEAN" methodologies integration on Industry 4.0 are another goal for the organizations to allow implement symbiotic collaborative systems between human workers, machines, and artificial intelligent entities. Another used technology is the industrial "exoesqueleto”.  Smart Factory: Also, there are consider as Intelligent factories, factories 4.0 or connected factories. All these same terms represent the same type of factory. Factories with connectivity machines between, devices and people, who offer new way of demand detect, re-design the supplies chains, manufacturing processes and information flows in real time, affecting anytime, any element on value chain, deleting any limit between demand, design, production, and supply. Another interesting approach is the Everis an NTT DATA Company (2021) study, which is, in author’s opinion very pragmatic that concerns to the industry 4.0 concept. As the before mentioned authors, this author also describes the evolution concept, since the first machines that mechanized some of the work of our ancestors until the early days of automation, when robots and machines began to replace human workers on assembly lines, passing by for the electricity, the assembly line, and the birth of mass production “Era”, and by third "era" of industry with the advent of computers. The big difference of this approach is this author puts the aim of study in to the “SMART Factory” concept. Industry 4.0 introduces what has been called the “Smart Factory”, in which cyber-physical systems monitor the physical processes of the factory and make decentralized decisions. Now, it’s entering Industry 4.0 where computers and automation have come together in a whole new way, with robotics remotely connected to computer systems equipped with machine learning algorithms that can learn and control robotics with very little input from human operators. In this context, other authors like Zhou eta (2015), Mohamed (2018), Rodal (2020), also has the same similar main objectives of Industry 4.0, mentioned before by Montero (2020): (I) Monitoring of machines and processes; (II) Simulation of manufacturing processes; (III) Zero-defect manufacturing; (IV) Customised manufacturing; (V) Optimisation of energy consumption; (VI) Offering new services 36 to customers; (VII) Increasing product information; (VIII) Improving product distribution; (IX) Enriching workers' skills; (X) Avoiding accidents at work. The above leads us to conclude that the application of Industry 4.0 can improve the competitiveness of organisations. Thus, the application of new technologies, the digitalisation of production operations and the management of data from machines, processes and products improves the performance of operations. For example, it favours the optimisation of resource consumption, improvements in the control and maintenance of machines or the reduction of non-productive times and product failures. But Industry 4.0 also has an important impact on people's motivation and working conditions. Moreover, there is no doubt that the introduction of the above technologies will pose significant challenges at management, organisational and supply chain level, as the integration of large amounts of data from related value chain activities becomes necessary (Chiarini, 2020). Specifically, other challenges that need to be addressed are those related to cybersecurity, data protection, training and qualification of workers or the economic costs of the necessary digital transformation (Zhou et al., 2015; Rodal, 2020). For all these reasons, Sony and Naik (2020) point out the need to consider Industry 4.0 as a strategic issue that should involve the involvement and commitment of workers and senior management. Finally, Calabrese et al. (2021) conducted an analysis summarising the main weaknesses, threats, strengths, and opportunities (SWOT analysis) that organisations that decide to implement Industry 4.0 may encounter (see Table 5). 37 SWOT element Description Strengths Cost reduction I4.0 technologies help reduce production costs Reduction of production time I4.0 technologies enable shorter production times Flexibility of the production line I4.0 technologies enable the cost-effective production of highly customised consumer goods in small batches ( size one batch production ). Productivity I4.0 technologies boost productivity Profitability I4.0 technologies increase profitability Competitiveness Companies adopting I4.0 technologies improve competitiveness in their sector Quality of production I4.0 technologies improve production quality Exchange of information I4.0 technologies facilitate the dissemination of information throughout the value chain and thus drive decision making. Weaknesses Reorganisation of employees Workers need to be reorganised to take advantage of I4.0 technologies. Reorganisation of the factory Factories must redesign to take advantage of I4.0 technologies Major investments I4.0 requires high investments with uncertain returns. Rapidly evolving technologies make these investments even more risky. Management of complex systems The management of the many interconnected machines is complex. Information management The information generated by sensors is difficult to manage and requires specific software skills. Monetisation of information Companies can collect information, but still find it difficult to exploit it for commercial purposes. Product customisation Companies find it difficult to set up customised small batch production lines. Customer orientation Companies do not have the right customer orientation to translate customer needs into effective solutions. Opportunities Vertical integration I4.0 technologies create opportunities for companies to vertically integrate their businesses Horizontal integration I4.0 technologies create opportunities for companies to integrate their businesses horizontally Customer sensitivity I4.0 technologies create opportunities for businesses to learn more from their customers Competitiveness outside the sector I4.0 technologies facilitate the development of new profitable business models outside the company's core business sector, e.g. service - focused business models. Threats Communication standards Different communication standards between machines from different suppliers threaten the automation of the production process. Data security I4.0 systems are vulnerable to cyber-attacks, resulting in serious threats to data security in the development of this paradigm. Network infrastructure Regionally limited bandwidth threatens the spread of I4.0 Legal framework Weaknesses in legal and regulatory frameworks threaten the adoption of the I4.0 paradigm, e.g. in terms of ownership and protection of company data, liability, and restrictions on trade. Competences The general lack of skills needed to manage I4.0 technologies threatens their adoption. Table 5. Elements of the SWOT analysis (Calabrese et al., 2021). With the SWOT analysis presented above by Calabrese et al. (2021), it was possible to identify the main challenges presented and encounter nowadays in our organizations (external and internal), when it’s pretended to implement industry 4.0. 38 3.2.3 New Emerging and Enabling Technologies for Industry 4.0 There are some authors who approach this theme in different ways. Below, we list some of random technological concepts described by some authors, where we´ll try to group and systematize in next points, according to some selected authors, who those that seems make us most sense. To know: No. Technological Concepts By Authors 1 Analytics and artificial intelligence Chiarini (2020); Vaidya et al (2018) 2 Robot and collaborative robot (COBOT) Chiarini (2020); Pereira and Romero (2017); Vaidya et al. (2018); Everis an NTT DATA Company (2021) 3 Reality and smart human interface (SHI) Chiarini (2020); Everis an NTT DATA Company (2021) 4 Smart Factory Lasi et al. (2014); Everis an NTT DATA Company (2021) 5 Cyber-physical Systems (CPS) Lasi et al. (2014); Pereira and Romero (2017); Sony and Naik (2018); Vaidya et al. (2018); Xu et al. (2018) 6 Self-organization Lasi et al. (2014) 7 New systems in distribution and procurement Lasi et al. (2014) 8 New systems in the development of products and services Lasi et al. (2014) 9 Adaptation to human needs Lasi et al. (2014) 10 Corporate Social Responsibility Lasi et al. (2014) 11 Internet of Things (IoT) Pereira and Romero (2017); Sony and Naik (2018); Vaidya et al. (2018); Xu et al. (2018); Everis an NTT DATA Company (2021) 12 Industrial Internet of Things (IIoT) Vaidya et al. (2018) 13 Internet of Services (IoS) Pereira and Romero (2017); Sony and Naik (2018) 14 Big Data Pereira and Romero (2017); Vaidya et al. (2018); Everis an NTT DATA Company (2021) 15 Claud Manufacturing and Augmented Reality Pereira and Romero (2017); Sony and Naik (2018); Vaidya et al. (2018); Everis an NTT DATA Company (2021) 16 Cloud computing Sony and Naik (2018); Vaidya et al. (2018); Xu et al. (2018); Everis an NTT DATA Company (2021) 17 Industrial Integration Xu et al. (2018); Everis an NTT DATA Company (2021) 18 Enterprise Architecture Xu et al. (2018) 19 SOA Xu et al. (2018) 20 Business Process Management (BPM) Xu et al. (2018) 21 Industrial Information Integration Xu et al. (2018); Everis an NTT DATA Company (2021) 22 Radio frequency identification (RFID) Sony and Naik (2018) 23 Enterprise resource planning (ERP) Sony and Naik (2018) 24 S ocial product development Sony and Naik (2018) Table 6. New emerging technologies for Industry 4.0 by authors. Source: Owner Driven of Industry 4.0 concept, Everis an NTT DATA Company (2021) in its study, and according to its vision considered different axes and paradigms of Industry 4.0, which, well combined, will empower companies both quantitatively and qualitatively in the digital transformation process. So, in this case, 39 added the most common enabling technologies for Industry 4.0, which was mentioned by other authors on the table above in a transversal way, whose aggreged named “paradigms”. According to Everis an NTT DATA Company (2021), that new “paradigms” allows to change the current way of thinking and working in the industry, and are organized by: o DIGITAL SUPPLY CHAIN: The key to this critical element of Industry 4.0 is big data analytics. Across this, is possible to know and to prescribe how the supply chain should operate in the market. So, derived from it, the goal of the Digital Supply Chain is to fully integrate and make visible every aspect of the movement of goods. o DIGITAL MANUFACTURING: Digital Manufacturing is the use of an integrated computerbased system comprised of simulation, 3D visualization, analysis, and collaboration tools to create product definitions and manufacturing processes simultaneously. o DIGITAL PRODUCTS: A digital product is simply a product or tool that you create once and can be sold many times. It is delivered digitally to your customers and is characterized by the step from product marketing to customer-tailored product service. Examples include digital media, such as e-books, downloadable music, Internet radio, Internet TV, and streaming media; fonts, logos, photos, and graphics; digital subscriptions; online advertisements (purchased by advertiser); internet coupons; electronically traded financial instruments; downloadable software (digital distribution) and mobile applications; cloud-based applications and online games; virtual goods used within virtual gaming economies and online communities e-learning (online courses) as well as the growing offering of user-tailored services; o DIGITAL CORPORATE: The Digital Transformation also refers to the set of activities that, through social tools, allow establishing connections between employees, the available information, and the company's business processes. By all standards and metrics, the digitalfirst approach enables corporate communications and IR capabilities to exceed user expectations like never before, ensuring they come back again and again. These are some of the most important benefits for companies that adopt a strong digital corporate communication strategy. o “PARADIGMAS”: As was referred by Everis an NTT DATA Company (2021), “paradigms” added the most common enabling technologies for Industry 4.0, that allows to change the current way of thinking and working in the industry, as like: Autonomous Robots; additive manufacturing; Internet of Things; cybersecurity; Mobility; New human interfaces; Horizontal and vertical integration; Big Data and Analytics; Simulation; RPA; blockchain; clouds. According Everis an NTT DATA Company (2021), the traditional industry is evolving towards Industry 4.0, across by: visibility, transparency, predictability, and adaptability. Also, according to our selection and for Xu et al. (2018), various technologies or techniques can be used for implementing Industry 4.0. These technologies include cyber-physical systems (CPS), Internet of 40 Things (IoT), cloud computing, blockchain, industrial information integration and other related technologies. For Vaidya et al. (2018), the main drivers to Industrial 4.0 and enabling technologies are also Internet of Things (IoT), Industrial Internet of Things (IIoT), Cloud based manufacturing and smart manufacturing, which helps in transforming the manufacturing process into fully digitized and intelligent one. Finally, and according to Sony and Naik (2018), the main feature of Industry 4.0 is cyberphysical systems (CPS). This last author put the emphasis of enabling technology in that issue (CPS), which involves some associated technologies: Internet of Things (IoT), Internet of Service (IoS), cloudbased manufacturing, radio frequency identification (RFID), enterprise resource planning (ERP), social product development. Also, we consider the terms, approach and Industry 4.0 definitions from Pereira and Romero (2017). Another approach of the enabling technologies and tools that enable the implementation of Industry 4.0 in organisations can be very varied (Vaidya et al., 2018; Xu et al., 2018; Olsen and Tomlin, 2020; Rosal, 2020), the most widespread being the following (Figure 1): Figure 1. Key enabling technologies and tools for Industry 4.0.  Internet of Things (IoT): Will be developed on the subtitles following bellow by the author selection.  Cyber-Physical System (CPS): Will be developed on the subtitles following bellow by the author selection.  Cloud Computing: Will be developed on the subtitles following bellow by the author selection. 41  Big data. It refers to the capture, management, processing and analysis of data or combinations of data sets generated by users or machines whose size, complexity, variability, and speed of growth make it difficult to use conventional software, technologies, and tools. There are terms related to big data such as Smart data, Data mining or Business intelligence. Smart data refers to the fact that of the immense amount of data that can be obtained, only a few are valuable, provide value and can offer competitive advantages. Data mining is a process of extracting relevant information from large amounts of data to discover patterns and trends that make the information more understandable. Business intelligence is the application of technological processes and tools to transform the data stored in the company into useful information for decision making.  Artificial Intelligence (AI). Software that can copy the cognitive functions of humans to learn and solve problems. AI does not learn on its own, it must be supplied with data and information to acquire knowledge and learn. In this aspect, the role of people is critical, to establish the areas in which AI should focus learning.  Virtual reality (VR) and augmented reality (AR). VR is the virtual recreation of an environment and the possibility of visualising it through a screen, visors, glasses, or special helmets. AR, on the other hand, is an instrument that shows us additional information to what we are observing, i.e., layers of information that are superimposed or added to the real image.  Additive manufacturing (3D printing). This consists of the design of objects in digital 3D format for their subsequent manufacture by adding layers of a material until the final product or object is created.  Industrial robotics. Fixed or mobile, automatically controlled, and reprogrammable multifunctional devices that replicate human movements. They are used for operations that are hazardous, require greater force or precision than a person can provide, or when continuous operation is required. There are other technologies that are becoming increasingly important in the context of Industry 4.0, such as drones and blockchain. Drones allow us to obtain information from places that are difficult to access and without endangering the integrity of workers (Rodal, 2020). The blockchain facilitates the exchange of information between members of a platform in a secure and non-centralised way on a single server. The information is encrypted so that network participants have access to the same data, but cannot modify it. This technology, for example, ensures the veracity and reliability of product and process traceability data (Olsen and Tomlin, 2020). Calabrese et al. (2021) identify 9 categories of enabling or facilitating technologies for Industry 4.0:  Production technologies: This class includes autonomous, communicative, and cooperative robots installed on the production line and additive manufacturing technologies such as 3D 48 The “Maturity Models” in the industry 4.0 also could be used in each stage of the digital transformation to help to identify where gaps exist, establish key areas to focus on, and where to start. Also, Sütőová et al. (2023) tell us, several Industry 4.0 Maturity Models have been published by academics and consulting companies (e.g., BCG, Pwc, McKinsey and Company) with the main goal to measure the success across different kind of metrics, scopes and dimensions. Industry 4.0 Maturity Model was published by the National Academy of Science and Engineering (“acatech”). This model is based on six stages (or steps) that help companies navigate from their early industry 4.0 transformation stage (with basic requirements) to full implementation. This maturity stages allowing a company to delegate certain decisions to IT systems are shown below in Figure 2. Four organization structural areas of resources, information systems, culture and organisational structure are assessed, and necessary capabilities are defined for each structural area. According to this model, the identification of the current Industry 4.0 maturity stage is based on its capabilities. Those capabilities are determined based on functional areas processes: Development, Production, Logistics, Services and Marketing & Sales that are assessed. Also, for this author, the present model helps to create a digital roadmap tailored to the needs the of organization. Figure 2. Industry 4.0 maturity Stages. Source: adapted from Sütőová et al. (2023). Bellow it´ll be present and selected some of few examples of other Industry 4.0 maturity or readiness models:  Digital Compass developed by McKinsey and Company (2015) consists of eight basic value factors (dimension) and 26 practical elements of Industry 4.0. Quality is one of the direct factors represented by elements such as digital quality management, advanced process control and 49 statistical process control. Other drivers in the model are the match between supply and demand, time to market, service/after-sales, resources/processes, use of assets and labour.  Industry 4.0 Readiness Model developed by Impuls (2015), Foundation of the German Engineering Federation Measures Industry 4.0 on the 7 levels of readiness. The model consists of 6 main dimensions and 18 related elements. The main dimensions are Strategy and organization, Smart factory, Smart operations, Smart products, Data-driven services, Employees, and allows self-assessment and calculation of the industry 4.0 scorecard. Also, It helps to identify areas that will need improvement and intervention.  Industry 4.0 Maturity Model developed by Schumacher et al. (2016). This model is formed by 9 dimensions and 62 elements. Also, and according to Schumacher et al. (2016), the main dimensions are Strategy, Leadership, Customers, Products, Operations, Culture, People, Governance, Technology, and the evolutionary path of each element it consists of five maturity levels.  The Digital Readiness Assessment Maturity Model (DREAMY) developed by De Carolis et al. (2007), defines 5 main functional areas, namely: Design and Engineering, Production Management, Quality Management, Maintenance Management, Logistics Management, with quality management represented by two elements: Product testing and quality management in production. On the other hand, the degree of preparation is assessed using a measuring instrument, defining 5 levels of preparation, where the areas mentioned above are assessed within the context of 4 dimensions, which are part of the model: process; monitoring and control; technology; organization.  The industry 4.0 Maturity Model developed by Santos and Marinho (2019) is composed of five main dimensions, namely: Organizational Strategy, Structure and Culture, Workforce, Smart Factories, Smart Processes, Smart Products and Services, where each dimension is described by characteristics and capabilities that are assessed using six maturity levels. According to Sütőová et al. (2020), there are still other Industry 4.0 maturity models that were developed by academics or consulting organizations, such as studies by Deloitte (2018), Klötzer and Pflaum (2017) and PwC (2019). That models created by these academics and organizations are so-called holistic models, which allow evaluating and using Industry 4.0 elements from all forms and perspectives and, therefore, deriving comprehensive success factors. There are also Maturity Models that focus on specific areas of Industry 4.0 application or on a limited number of aspects also related to Industry 4.0. Examples of this are logistics, supply chain, or information technologies. For Krishnan et al. (2021), the first step before implementing Industry 4.0 is to assess the extent to which organisations can realise the potential offered by Industry 4.0 technologies (Krishnan et al., 2021). Hizam-Hanafiah et al. (2020), following a systematic literature review, identified six main dimensions used by Industry 4.0 readiness models: Technology, People, Strategy, Leadership, Process, and 50 Innovation. Sony and Naik (2019b), also through a systematic literature review, found six key Industry 4.0 implementation factors: organisational strategy, level of organisational digitisation, degree of supply chain digitisation, smart products and services, employee adaptability with Industry 4.0, top management involvement and commitment. According to Sony and Naik (2018), the main thing to implement Industry 4.0 on Organizations, is to know if they already are readiness to received it. For that, this author considers the key ingredients for assessing the readiness of the organizations for Industry 4.0, the SLR methodology of Tranfield et al. (2003), that was deployed could achieve the goal of adding to the extant knowledge. Furthermore, for Sony and Aithal (2020), the first step before applying Industry 4.0 is to understand (evaluate) whether the organization is ready to receive and implement Industry 4.0. There is a wide range of models for evaluating the industry 4.0 model, however, recent research suggests that none of them measure the current dynamics of the business environment. Therefore, it is necessary to research and design the "Industry 4.0 readiness model". This is the most important step in the entire process, because if the evaluation results are above average, it is concluded that the organization can venture into implementing Industry 4.0. This model describes “the extent to which an organization is prepared for a successful implementation of Industry 4.0”. For this author, and for Felch et al. (2019), the industry 4.0 readiness model is extremely important for organizations, as only in this way will it be possible to understand the current position of the organization and the necessary change to implement for the application of Industry 4.0. In the sense of implementing Industry 4.0, this can be seen as a management tool, which will help in realigning, reconfiguring, and renewing the capabilities and capabilities of organizations. According to Antony et al. (2021) and with Krishnan et al. (2021) and Rajnai and Kocsis (2018) the first step to take before implementing Industry 4.0 is to assess whether an organization is ready or able to receive the implementation of Industry 4.0. Therefore, according to Hizam-Hanafiah et al. (2020), and considering several specialized literatures that address the topic in different forms and models, the industry 4.0 readiness model is described as: "The degree to which organizations can take advantage of Industry 4.0 technologies". Also, according to this author, in these models’ organizations can be classified according to their status: "not ready" or "almost ready", varying depending on the dimensions of the industry 4.0 model according to Hizam-Hanafiah et al. (2020) and Gokalp et al. (2017). For Sony and Naik (2019b), there is currently a common understanding regarding the dimensions of the industry 4.0 readiness model, which is why it is essential to answer the following question: "What are the dimensions of the industry 4.0 readiness model"? More recently, Antony et al. (2021), through a series of surveys of managers, identified ten key or readiness factors for the effective implementation of Industry 4.0 in organisations: (I) technological readiness, (II) employee adaptation to Industry 4.0, (III) smart products and services, (IV) digitisation 51 of supply chains, (V) digital transformation of the organisation, (VI) strategic readiness for Industry 4.0, (VII) innovative business model for Industry 4.0 , (VIII) leadership and top management support, (IX) organisational culture, and (X) reward and recognition systems. Finally, and for this study, and to try to answer to the mentioned issues and research questions above placed by these authors, was considered the approach to the SLR methodology of Tranfield et al. (2003) by Sony and Naik (2018), even there are other approaches to describe and analysed this theme: 3.2.4.1 Readiness of organizational strategy For Sony and Naik (2018), the industry 4.0 scope it encompasses every aspect of organization, sector and society. So, the impact of Industry 4.0 on organization strategy will be decisive. A long-term, the industry 4.0 impacts the company between Organization and Nature: this includes developments in resource efficiency and sustainability of manufacturing systems; organization and local communities. This leads to increased geographical proximity and acceptance, and integration of customers in design and manufacturing processes, organization, and value chains. So, the organizational strategy will be significant in each of these mentioned issues above, and consequently, it will be one of the most important ingredients for assessing the readiness of the organizations for Industry 4.0: o “Organizational strategy for implementing Industry 4.0 will have to take into consideration the anticipated changes of the relationship between (a) organization and environment; (b) organization and communities; (c) organization and value chains; and (d) organization and humans apart from the technical aspect of implementation.” o “Throughout the implementation of Industry 4.0, there should be an organizational strategy for the technical aspect of implementation, e.g., ICT-enabled technologies which primarily allow networked manufacturing systems, implying interoperable systems, information interchange and decentralized decision making and control.” o “Most of the elements of organizational strategy like vision, mission, values, long- and short-term goals, action plans, KPI’s, SWOT analysis, etc., will change with the implementation of Industry 4.0.” According to Antony et al. (2021), for Industry 4.0 to be successful organizations must devise the strategy and deploy technologies, principles, and systems to strategically achieve them. For that, “Industry 4.0 implementation changes the long term relationship in terms of (1) organization and nature (in terms of resource efficiency and sustainability in manufacturing systems) (2) Organization and local communities (increased integration of customers in the design process, reach of wider customer base etc.) (3) organization and value chains (enabling mass customisation due to distributed and responsive manufacturing and collaborative processes) and (4) Organizations and humans (in terms of improved human-machine interfaces and work conditions) (Santos et al., 2017; Sony and Naik, 2019b).” Big data is also considered in this equation to design the organizational strategy and implement the industry 4.0 52 as competitive advantage. So, for those authors the readiness of organization strategy to implement Industry 4.0 must consider these factors while implementing Industry 4.0. 3.2.4.2 Level of digitization of the organization According to Sony and Naik (2018), the level of digitization of organizational assets can be termed as the percentage of assets which are equipped with sensors, which sense the various parameters of relevance. The level to which an organization uses sensor data is the same measure integrated for greater transparency in planning and operation. In the Industry 4.0 implementation process, where the production is highly automated, the smart workpieces will control and monitor the production process on their own, resulting in less human intervention. Resuming, and for the author, the level of digitization of the organization assent in the four points below: o The cyber-physical assets of integration degree within an organization will determine Industry 4.0 implementation success. o Automated data management from the organization assets will lead to better automated decisionsmaking models using Industry 4.0 within the organization. o By integrating organization assets, e.g., organization systems with machines, the industry 4.0 will optimize the utilization of the resource. o The IT organization systems should be compatible and support all organizational processes leading to the better Industry 4.0 implementation. Antony et al. (2021), also addresses on his article: “Conceptualizing Industry 4.0 readiness model dimensions: an exploratory sequential mixed-method study”, how the digital transformation of the organization can be viewed, as the degree of integration of digital technologies into all business areas of the organization. Also, it could be viewed as the usage of digital technologies to transform different functional departments and associated systems and processes within these functions of organizations such as production, purchasing, marketing, accounting, HR, and finance (Sony and Aithal, 2020b). For this author, and according to Verhoef et al. (2021), the organizational digital transformations is viewed as an integrated process with three phases: digitization, digitalization and digital transformation, where the first one is transforming analogue data of an organization into a digital format and transmitting such information, the second one is the use of ICT to alter existing business processes, where the focus is to improve business processes to improve the customer experience and the last one describes organization-wide new thinking which results in new business models. Concluding the Antony et al. (2021) thinking and approach, the digital transformation of an organization impacts the whole organization dimension rather than changing simple organizational processes or tasks. The extent to which an organization is digitally transformed will determine how ready an organization is to implement Industry 4.0. 53 For this study we maintain the Sony and Naik (2018) approach as study organization. 3.2.4.3 Extent of digitization of supply chain One of presupposed of the industry 4.0 is all operations digitization within the entire supply chain and the physical systems integration with cyber worlds. So, according to Sony and Naik (2018), and based in other authors, a large amount of data acquisition, data interpretation and control is possible so that all the elements in the supply chain can benefit immensely, resulting in new production planning models in those organization´s supply chains. Thus, the smart workpieces moving along the supply chain will create the need for better production planning and control models based on the real-time big data. So, for the author, in an Industry 4.0 context, the supply chain in smart factories has a dynamic structure which changes as per the needs of customers, developing according to this author, a new algorithm based on the systematized key factors below: o Supply chain management in Industry 4.0 will encounter challenges such as real time, the intensive use of data and the dynamic structural organisation between the various elements of the supply chain, resulting in the need to establish new algorithms. o For Industry 4.0 to be more successful, autonomously guided workpiece systems must be implemented throughout the supply chain using intelligent logistics. o During the implementation of Industry 4.0, self-reactive processes in all elements of the supply chain will result in better alignment of flows, integration of functions, coordination of processes, design of complex systems and management of resources. o For Industry 4.0 to be more successful, IT system security and the implementation of cloud data management will result in the protection and management of the data of all stakeholders in the supply chain. A similar approach has Antony et al. (2021). For those authors, and according to Buyukozkan and Goçer (2018), Supply chains are defined as a “series of interconnected activities that involve the coordination, planning and controlling of products and services between suppliers and customers”. The traditional supply chain is made of a series of discrete siloed steps from supplier to consumer. The digitalisation of supply chains will result in breaking down the walls and transforming the supply chain into an integrated system that would run flawlessly. The digital supply chain is built on both digital transformation and smart technologies. The role of digital technologies in the digital transformation of the supply chain is the key element and hence organizations need to improve the level of technical adaptability and implementation of digital technologies (Frank et al., 2019; Pramanik et al., 2019). Therefore, organizations whose supply chains are digitalized stand a better chance of being ready to implement Industry 4.0. 54 3.2.4.4 Smart product and services t’s well Known the products and services are important components for the industry 4.0 success. For Sony and Naik (2018), given this key element is not less considered then the others refereed elements before. So, according to the smart factory concept, the Products are assembled from smart workpieces which are modular and "plug" compatible. These are equipped with sensors, RFID, communication interfaces, GPS, etc., to collect data from the environment and their own status about production. These data will be processed on cyber systems to guide the path on production system. Still for the author, and according to Industry 4.0 environment concept, the products integration, physical production systems and cyber technology, will enable the monitoring, self-regulation, and resource´s optimization to manufacture individual products, systematized their implementation in the following points as the author: o For a successful implementation of Industry 4.0, the integration of products and services with the organisation's other CPS should enable monitoring, self-regulation, and optimisation of resources. o For a successful implementation of Industry 4.0, the product and service offering in organisations must be designed in an innovative way to sell an integrated range of products and services that add use value for the customer (Servitisation concept). o Due to the implementation of Industry 4.0 and to create new service opportunities, the integration of the organisation's CPS must be integrated with the end customers. o To design services that fulfil customers' growing expectations of the product-service system, the principles of service management must be integrated into Industry 4.0. Antony et al. (2021) describes in their article related to the smart products and services to implement Industry 4.0 in Organizations, divided in three components, as the followed, according to Lichtblau et al. (2015), Sony and Naik (2019b), how ready an organization is to implement industry 4.0 is defined (Porter and Heppelmann, 2015): 1. Physical components, e.g. electrical and mechanical elements. 2. Smart components, e.g. microprocessors, sensors, data storage, controls, software, embedded operating system, and digital user interface. 3. Components of connectivity, e.g. such as ports, antennae, protocols, and networks that allow communication between the product and the product cloud, which run on remote servers and contain operating systems external to the products. Organization has the three components incorporated, the more ready the organization will be to implement Industry 4.0 as Lichtblau et al. (2015), Porter and Heppelmann (2015) and Sony and Naik (2019b) defend. As for smart services, Antony et al. (2021) describes the following, according to Allmendinger and Lombreglia (2005) and Wunderlich et al. (2015), respectively: 55 Smart Service has three fundamental elements that are real-time data collection, continuous communication, and interactive feedback. The smart object of the smart service can be an individual customer (e.g. monitoring healthcare), a group of customers (e.g. monitoring of family homes) or a company (e.g. monitoring of industrial equipment). Lastly, Organizations can use a collected information through smart objects to improve their offering services and permit benefit from personalized service features for the customers. 3.2.4.5 Employee adaptability with Industry 4.0 Inevitably, industry 4.0 was bring to the market labour reality enormous changes. According to Sony and Naik (2018), implementing a new working environment, where in the most of times, traditionally done by the workers, will now be done by the CPS in the smart factory. Those changes force the workers to look for by new abilities or making a skill upgrades of higher order nature, depending “worker survivor” by those upgrades. Those new abilities will be at the IT and critical thinking level. Still the author, due to the implementation of Industry 4.0, there will be changes in the traditional nature of employment and the structure of work. These include changes in individual responsibilities, assignments, and relationships. So, the degree of employee adaptability will increase as the changes in workplace change too. Systematizing by the Sony and Naik (2018) author: 1. “Smart employee adaptability models will have to be developed to predict the adaptability of employee during the implementation of Industry 4.0.” According to Antony et al. (2021), through automation and data integration, Industry 4.0 digitally transforms an organization to meet its goals. Increased automation in conventional wisdom will suggest workerless production or less human interaction (Sony and Aithal, 2020a). Recent studies suggest that implementation of Industry 4.0 will result in employees requiring a new skill set, however, employees will be critical for the success of Industry 4.0 (Dworschak and Zaiser, 2014; Hecklau et al., 2016; Weyer et al., 2015). Industry 4.0 thus will have social (human-related) and technical (non-human) components coming together to pursue a common goal commonly known as a socio-technical system (Avis, 2018). Therefore, employees are as important as technology for the success of Industry 4.0 (Sony and Naik, 2020). However, with the introduction of manufacturing automation and COBOTS, low-skilled workers will temporarily suffer from a lack of employment opportunities. Similarly, advances in artificial intelligence, machine learning and software automation will impact human employability in an organization. Therefore, and make a conclusion, Industry 4.0 will have an impact on all levels of employment. 3.2.4.6 Top management involvement and commitment According to Sony and Naik (2018), the top management involvement and commitment will be imperative for the accomplishment of the industry 4.0 successful implementation. In other words, the 56 continuous support of top management will be one of the most important ingredients for the readiness of the organization for Industry 4.0, thus requiring the allocation of large resources for its implementation. Systematized by the Sony and Naik (2018) author: 2. “Interrelations among key readiness factors for Industry 4.0 in organization.” Interrelations among key readiness factors for Industry 4.0 in organization: According Sony and Naik (2018), Industry 4.0 have a big impact in the functioning of the organization, from the organization strategy to manufacturing and from the design of products and services to productivity, so organization strategy is the main driving force which can influence the level of digitization of the organization and the extent to which the products can be smart within the organization. Finally, and addressing the Antony et al. (2021), this author consider that leadership will be the most important aspect in guiding the organization firstly in the digital transformation process and later in leading the organization in the digital environment (Sony and Aithal, 2020a). So, according to Klein (2020), the characteristics of the leadership should passing by for the follow leader aspect, which must be assimilated by him are: (1) visionary, (2) networking intelligence, (3) adaptable, (4) motivating coach, (5) digital intelligence, (6) complexity master, (7) social intelligence, (8) democratic delegative, (9) agile, (10) learning from errors, (11) role model, (12) diversity champion, (13) decisive courageous, (14) creativity, (15) openness, (16) self-awareness, (17) ambidexterity, (18) knowledge-oriented, (19) digital talent scout, (20) employee-oriented, (21) business intelligence, (22) lifelong learner and (23) ethical. For Sony and Aithal (2020a), in this process, the leader will be the most important person who guides the organization, and your changes, in the industry 4.0 implementation. Another aspect is the top management support, not only in terms of resource allocation, but also understanding the strategic importance of Industry 4.0, taking tactical decisions for the successful implementation of Industry 4.0. Furthermore, for this author, top management support can help to get necessary resources such as facility, capital, IT, and human resources. In this point, the top management involvement is a key factor to implement the industry 4.0. Concluding, it´s possible to stablish a relationship between Industry 4.0 and Quality 4.0 implementation and its readiness for that in Organizations. Naturally, in this research, it´ll be aboard this theme in the Quality 4.0 section area. But it´s possible, starting now, with state-of-art help by some authors references, making some extrapolation and parallelism between Quality 4.0 and Industry 4.0 organizational implementation. So, it´s possible to starting to point some factors or reasons, could that make part of this study, also could be the aim of that. Fan and Niu (2023) show us in that qualitative study the implementation gap refers to the lack of comprehensiveness of Quality Management core practices implemented in an individual company against the benchmark practices. Jepperson and Swidler (1994) defines, cultural, what is broadly defined as a complex set of values, beliefs, norms, and expressive symbols, and contextual factors, as firm demographic, organizational factors, and 57 external environment also could make part of the reasons to a “good” or a “bad” implementation. Also, the Company ownership could be another factor that we appoint and contributes for the gap’s implementation. Also, according to this author, based on the qualitative analysis, a conceptual framework that links key contextual and cultural factors to the identified Quality Management implementation gaps was defined, justifying that gap. It will be viewed at the long of this study a parallelism between Industry 4.0 and Quality 4.0, particularly what its readiness, implementation, and performance concerns in organizations. Related to Quality 4.0, Fan and Niu (2023) tell us that Quality Management has implementation gaps, with a large implementation gap being evident as the comprehensiveness of Quality Management implementation compared to commonly recognized fundamental Quality Management practices is lacking. Like industry 4.0, in Quality 4.0, the lack of some of these implementation practices in organizations affects the success of that implementation. These Quality Management practices, as previously mentioned, include management leadership, product/service design, supplier quality management and customer focus, some of which are also "crossed" by key readiness factors for Industry 4.0. 3.2.5 Framework to usher towards industry 4.0 implementation In this section we’ll present a possible model to usher towards industry 4.0 implementation for digital transformation. As we could aboard before in Industry 4.0 technologies, the role of industry 4.0 (I4.0) technologies for organizations is to achieve a competitive advantage and mitigate disruptive emergency situations. we’ll referee again in this section. For that, we’ll support by the study of Dohale et al. (2022), and the introduction novel framework for formulating manufacturing strategy 4.0 (MS 4.0) that guides organizations to implement I4.0 successfully and to achieve competitiveness during normal and emergency situations. According to Zheng et al. (2021) and Zhong et al. (2017), Industry 4.0, which was triggered by the Internet, consists of a set of advanced digital technologies, namely IoT, blockchain, cloud manufacturing, big data, artificial intelligence, additive manufacturing, reconfigurable manufacturing, augmented reality, simulation, digital twin, etc. that aim to build and install smart factories. According to Wang et al. (2016), smart manufacturing, which is the central pillar of I4.0, encompasses these technologies and digitally transforms organisations in the quest to achieve market competences, as shown in Figure 3: 64 current industrial landscape through three main points: (1) digitization of production, (2) automation and (3) linking the manufacturing site in a comprehensive supply chain. In that way, Industry 4.0 consists in full network integration and real-time information exchange. The core of every industrial revolution is the productivity increase. However, the fourth industrial revolution goes further and besides increasing productivity, will affect the whole supply chain from product development and engineering processes to outbound logistics. 3.2.7.2 Products and services The products will become more modular and configurable, promoting mass customization to meet specific customer requirements. Hence, and according to Pereira and Romero (2017), industry 4.0 is characterized by innovation and introduction of new products and services as embedded systems that can become responsive and interactive, being able to be managed and track their activity in real-time, optimizing the whole value chain and providing relevant information about their status during their lifecycle. 3.2.7.3 Business models and market Also, for Pereira and Romero (2017), value chains are becoming more responsive, since Industry 4.0 promotes the integration between manufacturers and customers, allowing a closer interaction with customers and the adaptation of business models to market requirements. 3.2.7.4 Economy Pereira and Romero (2017) refer that, economy can be influenced by the advent of the new paradigm and the emerging technological developments. The digitization consists in convergence between physical and virtual worlds and will have a widespread impact in every economic sector. This will be the main driver for innovation, which will play a critical role in productivity and competitiveness. 3.2.7.5 Work Environment The most significant change regards human-machine interface, which embraces the interaction between workers and a set of new ways of collaborative work. For Pereira and Romero (2017), the number of robots and smart machines is increasing, and the physical and virtual worlds are merging, which means that a significant transformation is being launched in the current work environment. 3.2.7.6 Skills Development For Pereira and Romero (2017), the future work vision will demand for new competencies, and it is necessary to create opportunities for the acquisition of the required skills through high quality training. Industry 4.0 will lead to an increased automation of tasks, which means that workers should be prepared for performing new tasks. The same applies for engineering education that holds a large of potential to 65 train the professionals of the future and aware them about new technological trends and opportunities, as well as the managers that should adopt their management strategy to the new market requirements. Industry 4.0 represents an enormous potential in many areas and its implementation will have impacts in the entire value chain, improving production and engineering processes, enhancing the quality of products and services, optimizing the relationship between customers and organizations, bringing new business opportunities and economic benefits, changing the education requirements, and transforming the current work environment. 3.3 Quality 4.0 The term “Quality 4.0” results from integrating I4.0 into quality management practices (Sader et al., 2022). It can also be considered as the fourth phase in the evolution of quality, in which the approaches of the previous three evolutionary phases are taken over (Maranga and Taifa, 2023; Markatos & Mousavi, 2023; Saihi et al., 2023; Sánchez-Franco et al., 2023). The First Industrial Revolution was characterised by using the steam engine and the mechanisation of production. During this Industrial Revolution, quality was developed through Quality 1.0 and characterised by quality control and measurement. Until the mid-1940s, quality management was product-centred, with the fundamental objective of delivering error-free products to customers. To this end, products were first inspected, and defective items were separated. This stage was succeeded in time by Quality 2.0, which was linked to the 2nd Industrial Revolution. This Second Industrial Revolution is notable for the emergence of a broad constellation of technologies, management approaches, and the use of electricity that expanded mass production. The inefficiency of the previous system made it necessary to consider new modes of action that focused attention on products and processes, with the emergence of "quality control". Process controls saved time and money while improving quality. Later, during the 3rd Industrial Revolution, Quality 3.0 was developed, which meant opting for a strategic perspective on quality management. The Third Industrial Revolution started with the partial automation of industries through computers, which made the "customisation of the masses" possible. At this time, the increase in supply and competition generated a more proactive and preventive approach focused on processes to detect risks and problems before they arose (quality assurance). Finally, during the 4th Industrial Revolution, Q4.0 is characterised by digitalisation, new technology applications and massive data use (Ali and Johl, 2021; Maranga and Taifa, 2023). Q4.0 transforms quality management from a reactive or proactive strategy to "predictive" by fusing the power of information, connection, and big-data tools (Sader et al., 2022; Prashar, 2023). According to Joshi et al. (2024), with the start of the 4th Industrial Revolution (I 4.0), Q4.0 differs from its predecessors in integrating digitalisation and cyber-physical systems. Q4.0 now incorporates a contemporary methodology for guaranteeing the quality of products and processes, using innovative technologies, real-time data analysis and continuous improvement strategies, with the main objective 66 being to increase the efficiency, effectiveness and adaptability of quality management practices, across all sectors of activity. 3.3.1 Quality 4.0: Concept and Approaches The 4th Industrial Revolution or Industry 4.0 has given rise to a new stream of research concerning the future of quality management, called Quality 4.0 (Gunasekaran et al., 2019). Research claims that one of the most important impacts of new technologies, digitalisation and the massive use of data is occurring in the quality function of organisations (Antony et al., 2020; Sony et al., 2020; de Souza et al., 2022). In this regard, Küpper et al. (2019) point out how Quality 4.0 is a further component of the 'factory of the future', where the digitisation of manufacturing structures and processes will increase productivity and flexibility in manufacturing and throughout the supply chain. In addition, digital technologies can help improve quality by monitoring processes, collecting real-time data, or applying analytics that help predict quality problems and equipment maintenance needs. Digital tools also enable people to do their jobs faster, better and at reduced cost. However, the successful implementation of Quality 4.0 requires addressing not only technological but also strategic and cultural issues. Indeed, although companies recognise that Quality 4.0 can contribute to creating value for the organisation and its customers, few have defined a deliberate strategy and initiated projects for its implementation. According to Chiarini and Kumar (2022), in the era of the fourth industrial revolution, the quality profession faces the challenge of adapting and thus maintaining the relevance of this business function in the face of changes in the environment. For these authors, Quality 4.0 is redefining the approach to quality management to cope with information technologies, increased connectivity, and digitisation (vertical, horizontal, and end-to-end) throughout the supply chain and the use of large amounts of data for evidence-based decision-making. Along these lines, Souza et al. (2021) state that Industry 4.0 represents a new paradigm for manufacturing, in which traditional concepts of quality must assimilate the changes and prepare for new challenges. Thus, tasks that were previously performed by people become automated or computer- and even robot-assisted, reducing human involvement almost completely. However, for Küpper et al. (2019) technology is just one more piece in the quality transformation that must also focus on people and a series of soft skills such as change management, communication, or teamwork. For Zonnenshain and Kenett (2020) the discipline of quality and its management has stagnated in recent years, few innovative models for quality management are proposed and quality professionals have lost importance in organisational structures. The era of Industry 4.0 is therefore an opportunity for the quality movement to become a leading force, although it poses significant challenges to the quality function. These developments lead to the need to unite the potential of the technologies used in Industry 4.0 with the potential of human knowledge, experience, and skills. This necessarily implies a change in the way 67 quality management is understood and implemented, and new competences emerge, which are necessary for quality professionals to cope with these changes (de Souza et al., 2022; Sureshchandar, 2022). This view is also corroborated by Carvalho et al. (2024) who, in addition to also arguing that the concepts linked to Quality 4.0 (Q4.0) in the context of Industry 4.0 (I4.0) have lagged behind in terms of their evolution, implementation and use by organisations and other users of Q4.0, compared to other advances in I4. 0, proposes a process for designing and developing a roadmap that helps organisations achieve and develop the necessary capabilities to pursue the digital transition with a Quality-oriented perspective, in essence proposing a transition plan to Q4.0 within the framework of I4.0, which can be controlled, measured and managed, thus minimising its implementation and change costs. In short, for this author, the path to Quality 4.0 has been unclear and there is no roadmap to help organisations prepare for or navigate this transition. For him, it is important to understand this path and it can be designed and developed to guide organisations, thus enabling them to position themselves and make a smoother transition (Carvalho et al. 2024). Also according to Carvalho et al. (2024), when looking at the available literature, one of the most significant gaps at the moment is the lack of adequate tools to guide the activities of Quality-oriented organisations to navigate the I4.0 transition. Although several Industry 4.0 roadmaps and maturity models are available, there is a lack of dedicated tools to guide a Quality 4.0-orientated digital transformation, thus creating a world of research opportunities aimed at developing the field of Quality studies in an Industry 4.0 context.So, he then proposes the creation of a "Quality 4.0 Roadmap", in which he highlights at each stage the necessary capabilities that organisations need to develop and warns them of potential pitfalls, providing an original tool to help organisations achieve and develop the capabilities needed to pursue the digital transition with an operational and human-centred perspective, and also defines vital stages in the Quality 4.0 transition, identifying evolving requirements and marking boundaries and points of contact between technology and quality management (Carvalho et al. 2024). Conceptually, and according to Joshi et al. (2024), Quality 4.0 (Q 4.0) refers to the supervision of quality in the contemporary Industry 4.0 (I 4.0) scenario. The fusion of cutting-edge technologies of I 4.0 envisages Q 4.0 helping organisations to achieve extraordinary organisational performance, operational excellence and constant improvement through quality management systems, tools and methodologies, signifying a global shift in technology and manufacturing processes. For this author, globally, only a few companies have extensively and unequivocally explored and implemented Q 4.0 strategies, where for the majority of companies, it is still a challenge to understand, plan, implement and manage Q 4.0 to improve organisational performance. Joshi et al. (2024) further tells us that it is essential to have a secure understanding of the concept of Q 4.0, both on a theoretical and practical level, in order for the development and implementation to be successful, so addressing the current thoughts and experiences of professionals during this process becomes crucial. 68 Because of the way quality approaches have evolved, according to Joshi et al. (2024), for lagging economies it is necessary to explore their critical success factors (CSFs), motivations and factors that leverage technologies to create and implement Q 4.0 structures. In this way, this author proposes the creation of an evaluation model for TQM, in order to assess the critical factor for the Q 4.0 transition, in a context of manufacturing companies in lagging economies, where it improves the sustainable performance of these companies, while using digital technologies to optimise processes, reduce waste and improve product quality, with the aim of providing practical knowledge that manufacturers and governments can use to support successful economic transitions. According to the author, this approach is aligned with social, environmental, ecological and sustainability objectives. Succinctly, the study by Joshi et al. (2024) aims to defend the importance of the assessment framework in guiding the Q 4.0 transition in developing economies, highlighting the potential for achieving sustainable performance and competitiveness through the effective integration of data-based practices. 3.3.1.1 Concept of Quality 4.0 Business models, as well as management systems, will continue to change dramatically over the next decade. Thus, for modern quality management to contribute to these changes, it needs to continuously develop towards the concept of Quality 4.0. Although there is still a lot of uncertainty related to the possible approaches for the practical implementation of Quality 4.0 (Nenadál et al., 2022). For Antony, Sony, McDermott et al. (2021) and Sony et al. (2021), Quality 1.0 was assured by measurement and inspection. Quality 2.0 emerged with the industrial revolution and Taylorism theory, and quality was based on compliance with standards or specifications that set the levels of quality considered acceptable in processes and products. Quality 3.0 goes from Taylor to the end of the 20th century, and quality is considered a basic principle for companies. Special emphasis is placed on customer satisfaction, process management, continuous improvement, productivity improvement and the commitment of everyone in the organisation. The term Quality 4.0 was first used by Dan Jacob (Antony, McDermott and Sony, 2021) and since then there have been many attempts to define it. A first approach would be to consider Quality 4.0 as quality management in the era of Industry 4.0 (Sony et al. 2021), i.e. Quality 4.0 uses digital tools to manage quality (Antony, Sony, McDermott et al., 2021). Thus, production and service delivery based on digitisation and the massive use of information will create new opportunities for organisations to incorporate technological advances that enable them to achieve optimal levels of operational excellence, performance, and innovation (Sony et al., 2020). As a result, organisations will improve the quality of products and services and achieve customer satisfaction, which is a competitive advantage (Zonnenshain and Kenett, 2020). The following table lists some of the definitions of Quality 4.0 in the specialised literature. 69 AUTHOR(S) DEFINITION OF QUALITY 4.0 Schönreiter (2017) Real-time integration and synchronisation of manufacturing process data with quality management so that the entire value chain can be analysed and controlled to take the necessary measures to avoid production stoppages or product rejections. Aldag and Eker (2018) Integration of traditional quality management practices and techniques with new technologies in a way that results in an advanced collaborative environment in which management activities are driven by increased connectivity throughout the value chain, from the supplier to the end customer. Sony, Antony and Douglas (2020) Digitalisation of design quality, compliance and performance using new technologies. It is therefore a new approach to using digital tools to improve the ability of organisations to deliver products and services that meet customer needs and expectations. In addition, Quality 4.0 will drive improvements throughout the organisation's value chain and improve decision-making at all stages of quality management and improvement. Sader, Husti and Daroczi (2022) Expanded approach to quality management, where new technologies are integrated with traditional quality practices (quality control, quality assurance, quality management and TQM) to broaden the scope of quality management and improve the performance and ef fectiveness of quality practices. Antony, McDermott and Sony (2021) Use of advanced technologies such as IoT, CPS, Cloud computing to design, develop and maintain adaptive, predictive, self-correcting, and automated quality systems that, together with improved human interaction through quality planning, assurance, control, and improvement, enable optimal levels of performance, operational excellence, and innovation to achieve the organisation's mission, vision, and objectives. Table 7. Definitions of Quality 4.0 In this context, concepts related to Quality 4.0 are also emerging, such as TQM 4.0 (de Souza et al., 2022), which aims to align the competences of quality professionals with the changes proposed by Industry 4.0 and Quality 4.0, characterised using technologies in quality management systems. From the above definitions, it stands out that to implement Quality 4.0 and meet business objectives, it is essential that both technological and human components work in an integrated manner. Thus, Antony et al. (2021) points out that Quality 4.0 is a socio-technical system in which there must be a symbiotic relationship between HR and technology. Along these lines, Sureshchandar (2022) argues that while technology is vital for Quality 4.0, the "traditional" elements of quality management such as leadership, culture, customer orientation, quality systems, compliance, HR training and competence, analytical thinking, data-driven decision making, etc., are mandatory elements for transformation to take place in organisations. Finally, as Chiarini and Kumar (2022) and Sony et al. (2021) point out, Quality 4.0 is a concept that is at an early stage and there is a need to develop a theory to help conceptualise it and thus have a deeper and more complete understanding of the concept. 3.3.1.2 Approaches to Quality 4.0 In Mahin et al. (2024), it is discussed that as Industry 4.0 emerges, there are significant changes in organizations due to increased digitalization and automation. This increased digitalization allows companies to adopt advanced technology, leading to improved operational efficiency and performance (Santos et al., 2021 and Sony et al., 2020). Quality management, an essential aspect for any organization, 70 is also evolving in the era of Industry 4.0, referred to as Quality 4.0 (Q4.0). Dan Jacob introduced this concept, explaining that Q4.0 combines new technologies with traditional quality approaches (Sony et al., 2020). However, alternative definitions for Q4.0 have been proposed by other authors (Sader et al., 2022 and Watson, 2019). Essentially, Q4.0 refers to quality management in the modern context of Industry 4.0. As stated by Mahin et al. (2024), Q4.0 involves integrating innovative Industry 4.0 technologies with traditional quality management systems, tools, and techniques to achieve exceptional performance, advanced operational excellence, and optimal innovation (Antony et al., 2022 and Radziwill, 2018). For Sader et al. (2022) most research assumes that Quality 4.0 emerges as an influence of Industry 4.0. However, Quality 4.0 has also had its own development and can be seen as another phase in the evolutionary process of quality in organisations. Within the approach that considers Quality 4.0 as an extension of Industry 4.0, Johnson (2019) considers Quality 4.0 as a trend within Industry 4.0, which uses technology to monitor and control quality-related activities, moving quality from being a task of a single team, area, or department to being everyone's job. For, Seo and Lee (2019) quality management in the context of Industry 4.0 is more proactive, as, quality problems can be predicted earlier. In addition, the customer is no longer a mere recipient of products, but a key link in the product value chain. According to Kenett et al. (2020) in the era of Industry 4.0, quality focuses on data and information. Illés et al. (2017) highlight how Industry 4.0 technologies make it possible to collect previously unknown data and information on quality. The challenge for companies is to identify where, how, and what data to collect, as well as how to work with large amounts of unstructured data (big data). For Sony et al. (2020), what is important is that the technologies allow data to be collected throughout the entire product lifecycle, which facilitates the understanding of the needs of customers and other stakeholders, helping companies to achieve a better balance between product design, cost and added value. However, for Radziwill (2018) Quality 4.0 is a further stage of an evolutionary quality process comprising: (1) inspection and quality control to exclude products that do not meet requirements or specifications throughout production; (2) process control and quality assurance; (3) the holistic approach to quality or TQM; and (4) Quality 4.0, which is the result of acting in an intelligent environment and the instantaneous processing of production-related data. In this area, the continuous development of information and communication technologies (ICT) and digitalisation has driven this fourth phase of quality development. The characteristics of Quality 4.0 have therefore broadened the focus of quality from a local perspective aimed at excluding defective products or monitoring manufacturing processes to a more holistic perspective with intensive monitoring of all elements contributing to the production value chain (Sader et al., 2022). 71 As per Mahin et al. (2024), the continuous digitalization and automation of Industry 4.0 have made Quality 4.0 (Q4.0) crucial for integrating quality management with new technologies in the industrial and service sectors (Thekkoote, 2022). This development illustrates the ongoing necessity to enhance operational efficiency and meet the rising customer demands, which require strict monitoring of production processes to prevent errors and deviations (Antony et al., 2023 and Jordan, 2020). Despite the potential of Q4.0 to significantly improve organizational capabilities, its implementation and understanding in organizations are still in the early stages (Antony et al., 2022 and Chiarini and Kumar, 2022). According to Mahin et al. (2024), Quality 4.0 aims to achieve optimal quality standards in various sectors by integrating digitalization into quality management systems (Salimova et al., 2020). Hence, a comprehensive understanding of the practical and theoretical aspects of Q4.0 is essential for the successful implementation and integration of these systems in contemporary companies. 3.4 Systematic Literature Review (SLR) In line with what Orero-Blat (2022) says, and as advocated by other authors, theory building is a development process aimed at organising ideas to explain phenomena that are empirically testable to conceptualise that same idea. Thus, for this author and according to Chibucos et al. (2005), theory can produce new research, with a prior understanding of why academics in the past have explained the adoption of technology or innovation. As per Mahin et al. (2024), a systematic literature review (SLR) is a thorough analysis of existing publications that involves synthesizing published data using a systematic technique (Tranfield et al., 2003). According to Kraus et al. (2020), an SLR is a comprehensive assessment of current literature that uses a transparent and replicable approach to search the literature, assess its quality, and synthesize it while maintaining a high level of objectivity. SLR is a highly reliable strategy for browsing and analyzing a wealth of literature. Furthermore, it is essential to researchers as it effectively identifies deficiencies in previous academic work (Shamseer et al., 2015), as corroborated by Mahin et al. (2024) and highlighted in this research. According to Kitchenham and Charters (2007), to build a theory, the following sequential phases of the process should be observed: 1. Review protocol: This is nothing more than the phase in which the research question(s) are defined, to be able to select articles, data sources, inclusion, and quality criteria. 2. Inclusion and exclusion criteria: This is the stage where the criteria for reviewing the articles to be used are identified and selected, which must be clear and explicit. This means determining from which date they will be selected, which keywords will be used, which epistemological approaches will be considered, and which databases will be used. 72 3. Quality assessment: In this phase, the standards of scientific rigour, credibility and relevance will be reviewed, which should be defined in advance to verify the individuality and veracity of each piece of research. 4. Data extraction and synthesis of results: Finally, this is the phase in which the results obtained from the research are systematised, which, based on the literature review, should be compiled, and presented in a clear and rigorous manner. Also, according to Orero-Blat (2022), and for this research, the present systematic literature review (SLRs) will use and adapt the new protocols of Behera et al. (2019), which use, based on a comprehensive analysis of several authors, criteria for analysing previous reviews. In addition to being innovative, and according to Khan et al. (2021), these protocols are particularly effective for replicable systematic literature reviews. For Behera et al. (2019), the context of the investigation must be identified so that a systematic review of the literature can be carried out. In this specific case, and as discussed in the previous chapter, the research area of this work is clear: On the one hand, identify the sufficient preparation factors for the implementation of a 4.0 quality system, and on the other, equally identify its necessary factors. to prepare an organization to receive a quality system 4.0. In line with the relevant literature (Kraus et al., 2020; Bresciani et al., 2021), it will be following a rigorous process in selecting the sample of articles to analyse. The Orero-Blat (2022) proposal for the review comprised three main phases, as it could see forward on the figure 4 bellow: Figure 4. Systematic literature review (SLRs). Source: adapted from Behara (2019). Orero-Blat's guidance (2022), in the first phase of the systematic literature review (SLR), an attempt will be made to plan the review by outlining the research objectives, identifying the inclusion criteria, and selecting the appropriate databases. So, and according to Orero-Blat (2022), the research objectives identified for the literature review are as follows:  Identify and analyse the main research themes in the literature on the central constructs of this doctoral thesis.  Identify the main research gaps and limitations in this literature.  Discover the most promising avenues for future research in this field. 73  To record the available literature on the relationships between the central constructs of this research, which will be tested in the methodological part. The inclusion criteria used to select the articles for analysis were as follows: systematic literature reviews (SLR), bibliometric analyses, quantitative or qualitative studies on business organisation and IT structures. The keywords were chosen based on the main definitions of the review constructs and the databases analysed were only "Scopus" online platform as a database tool to collect the relevant information from the authors, such as reliable abstracts and citations, journal articles, relevant books, and dissertations and much more. This is supported by metrics and analytical tools. Still in relation to sources, and due to the importance of the topic, scientific journals will be considered, in addition to the websites already mentioned. Once planned, the review will be undertaken and developed. Another similar approach is proposed by Suárez et al. (2017) in their article, which presented the state of the art in quantitative research on the EFQM model, and a systematic literature review from the period 1991–2015 was carried out in impact journals belonging to the Journal Citation Reports (JCR) and SCImago Journal & Country Rank (SJR), using a selection of 53 papers, and aspects related to the purpose, nature and instruments of data collection, type of quantitative analysis employed, sector under study and main conclusions and analysed contributions. As a result, the study presented more than a dozen lines of future research. Based on this, and according to Sanchez-Meca (2010) and corroborated by Suárez et al. (2017), the following will be presented in this theoretical framework section, the systematic literature review (SLR), which is a type of scientific research that has the purpose of objectively and systematically integrating the results of empirical studies on a certain research problem, to determine the state of the art in a specific field of study, used to investigate what has been done and to determine future lines of research. So, and according to these authors, generally, but also in this case, the main reasons for using SLR are: (1) When researching a particular topic, it is insufficient to rely on just one article or a few of them. This is because other researchers may challenge the validity of the results obtained, as it is highly unlikely that a single article or a small number of articles can provide conclusive answers to all the research questions. Additionally, these articles may have significant methodological limitations that compromise the accuracy of the results and conclusions drawn. Therefore, it is advisable to consult a broad range of relevant literature to ensure a comprehensive understanding of the topic under investigation. (2) The number of scientific journals and articles is increasing, providing us with a vast amount of information to consider for research. However, it can be challenging to keep up with this overwhelming volume of data. In such cases, unbiased summaries of research on a specific topic can be highly beneficial. (3) The traditional way of reviewing literature (known as non-systematic or narrative) has two primary disadvantages. First, there is the researcher's subjective discretion in selecting and analysing articles 80 By presented the figure 6 above related to the process article selection, it could be seemed that all the articles came only from the Scopus (Elsevier) with 100% of the publications selected. In this research it was not considered other sources for the article selection as for e.g. Web of Science (WoS), Google Scholar or even World Public Library, where the work base for the article selection was only the Scopus (Elsevier) platform. Also, bellow it can be seemed in the figure 7 and 8 the documents by year selected and documents per year by source, respectively, according to the documents from six main publishers selected result: Figure 7. Documents selected by year. Source: Scopus (2024). Figure 8. Articles selection per year by source. Source: Scopus (2024). It can be seen the search selection result in different ways. Bellow, on the follow figures, it shown some of them, just like the Documents by author, documents by affiliation, documents by country or territory, documents by type and finally documents by subject area. All this search presentation comes from Scopus (Elsevier) platform and are generated and processed by it. 81 In resume way, it was possible to observe the characteristics of the origin of the selected documents (articles) for the SLR of this thesis. The analysis of the documents years was and focuses in the 2022 and 2023 years with the 28 and 46 publications analysed, respectively. Those two years represents most of the origin of the publications with over 78% of the total of the selected publications. This means this study was its focus on the most recent published documents, where ensure it a most recently “State of Art” research. In terms of “Documents by Source”, this quantitative SLR focused on the 6 most accredited journals in the research scope. Just it mentioned before was the follow: (1) “TQM Journal”, (2) “Quality Innovation Prosperity”, (3) “Sustainability Switzerland”, (4)” International Journal of Quality and Reliability Management”, (5) “Total Quality Management and Business Excellence”, (6) “Proceeding on Engineering Sciences”. Particularly, it could be observed the “TQM Journal” was the source that comes most of the documents, with a total of 21 publications in the global of the years in analysis, which was since 2019 to 2024, and with a representation about of 36% of the globally journals analysed. In terms of documents by author, below in the figure 9, also it could be observed that a total of 10 main authors which makes publications in this research field. Particularly, Antony, J. and Sony, M. represents the most study’s authors with 9 publications each other, respectively, with a representation of 33% of the authors globally journals analysed. Also, it was analysed in terms of documents by affiliation, below in the figure 10, it means that the local documents produced (Research Institution). For that, it was observed the 2 main origin Institutions was the “University of Galway” in Irland and “Khalifa University of Science and Technology” in Abu Dhabi, UAE, with 8 publications each other, respectively, with a representation of 31% of the total of documents by affiliation analysed. In terms of documents by country or territory, below in figure 11, also it was observed that a total of 10 main countries/territories which from publications in the research field. Particularly, India represents the country with more documents produced with 19 publications, with a representation of 21% of the globally countries/territories analysed. In terms of the type documents, just like the figure 12 show us, was considered a total of 95 documents distributed by articles and reviews with 82 and 13 documents, respectively. Also, this figure shows us musth more incidence for the articles. In terms of documents of subject area, just like the figure 13 show us, was considered a total of 8 main subjects’ areas, distributed by a total of 184 considered documents. It means that the main are consulted for obtained documents was the “Business, Management and Accounting” area with a representation over 34% of the globally subject areas with 65 documents. 82 Figure 9. Documents by author. Source: Scopus (2024). Figure 10. Documents by affiliation. Source: Scopus (2024). Figure 11. Documents by country or territory. Source: Scopus (2024). 83 Figure 12. Documents by type. Source: Scopus (2024). Figure 13. Documents by subject area. Source: Scopus (2024). On the following section it will make a quick analyse about what was the results of the quantitative search related to SLR and try to understand the correlation for the thesis research objectives. 3.4.5 Analysis of the SLR quantitative research Following it will be present the principal table (Table 8: Quality 4.0 Literature Review) that represents the result of the Systematic Literature Review (SLR) of our document search of this thesis research. According to parameters mentioned before, this table will show the 28 selected articles, where it could be seen the author of each article, the objectives and the research question associated of each study, the methodology used it and the objectives or goals to achieved with the study and its conclusions. Just it could be referred before, this table represents aim of the SLR. For the SLR analysis also it will be present an additional table (Table 9: Aims of the analysed research) where will present some considerations related to this SLR selection. In this point will present some comments related to the research objects of each study that connected to the central SLR central objectives. In addiction it will be presented another 84 tables which represents and shows us some research methodological issues, also with comments. Finally, will take a conclusion about of this SLR quantitative research analysis and try to indicate some possibles limitations of this research, motivations, and barriers for the Quality 4.0 implementation, also its challenges. 85 Author (s) Objectives/Research questions Methodology Main results and conclusions 1. Ali & Johl (2023)  Empirically analyse the effect of soft and hard Q4.0 factors on I4.0 readiness and current I4.0 practice.  Analysis of company size as a moderating factor  Questionnaire respondent: 228 small and medium-sized manufacturing firms in Malaysia.  Data Processing: PLS-SEM and Multigroup Analysis  Soft TQM practices (top management commitment and HR management) and hard TQM practices (process and information management and analysis) have a significant effect on I4.0 readiness.  Training and education do not have a significant effect on I4.0 readiness.  I4.0 readiness has a positive and significant association with actual I4.0 practice.  Company size significantly moderates the relationship between TQM practices and I4.0: larger organisations are more prepared than smaller companies to take advantage of the potential of I4.0 technologies. Smaller entities have limited resources, which translates into a lower ability to transform the potential of I4.0 into economic benefits and competitive advantages. 2. Antony, Kaul, Bhat, Sony, Kaul, Zulfiqar & McDermott (2023)  What are the critical failure factors in the implementation of Q4.0?  Qualitative study: semi-structured interviews  Participants: 14 quality experts from the industrial field.  Critical failure factors of Q4.0: Lack of management support, lack of alignment of Q4.0 projects with strategy, lack of understanding of the benefits of digitisation to improve quality, difficulties in finding educational courses and training in Q4.0, difficult recruitment and hiring of staff qualified in digitisation and new technologies, not being able to communicate the impact of Q4.0 implementation, time needed to train and learn how to use new technologies.  Aptitude towards change and management of the change process were the main critical failure factors identified. 3. Antony, McDermott, Sony, Toner, Bhat, Cudney & Doulatabadi (2023)  Deepen understanding of the concept of Q4.0  Understand the benefits and motivations for adopting Q4.0  Identify critical success factors, challenges, organisational readiness factors and competencies for Q4.0  Qualitative study: interviews  Participants: 11 senior managers and practitioners from leading Q4.0 deploying companies in different industries (seven countries on four continents).  Data processing: ATLAS Ti9 software  Q4.0 concept: systematic integration of I4.0 technologies with existing quality tools and methods, also incorporating the human factor, to achieve superior quality performance and higher levels of operational excellence.  Motivations: quality improvement, cost reduction, improved problem solving, effective decision making, and reliable and rapid data collection.  Benefits: improves customer satisfaction, ensures better quality products, and services and provides competitive advantage  Critical success factors: top management and leadership support, employee training or investment in technology infrastructure.  Challenges: getting management commitment to invest in technology and guide organisational strategy to implement Q4.0, cost of implementation, lack of financial, human and time resources, lack of understanding of the benefits of Q4.0 (information management, decision making), lack of understanding of the relationship of Q4.0 to organisational strategy and objectives.  Readiness factors: human resource management, culture of continuous improvement, resourcing and technology infrastructure, datadriven management, Q4.0 as a strategic issue  Core competencies for Q4.0: digital and data analytics skills, teamwork, communication, decision-making, problem-solving, project management, learning skills. 4. Antony, Sony, McDermott, Jayaraman & Flynn (2023)  What are the pros and cons of implementing Quality 4.0 in different organisations (manufacturing, services, SMEs and large enterprises)?  What are the organisational readiness factors for the adoption of Q4.0?  Qualitative and quantitative study: online surveys  Participants: 147 senior management professionals (manufacturing and service companies of different sizes, in the Americas, Asia and Europe)  Data processing: Atlas 7.0 (qualitative data) and percentages, frequencies, t-test, regression analysis (quantitative data).  Causes of non-adoption of Q4.0: Lack of resources (technological, financial and knowledge), Q4.0 is not linked to business strategy, benefits are not clear, high initial investment, use of other strategies and methods of QA.  More than 70% of the participating companies that have adopted Q4.0 were from the manufacturing industry.  The % of companies adopting Q4.0 was higher in services than in manufacturing.  Top five "pros" for implementing Q4.0: o Manufacturing: Big data for quality management, Reliable data, Improved productivity, Long-term cost and time savings, Increased customer satisfaction. o Services: Increased customer satisfaction, big data for quality management, Long-term cost and time savings, more accuracy and fewer errors, Improved decision making. o SMEs: Improved productivity, Reliable data, Faster and more transparent processes, big data for quality management, More accuracy, and fewer errors. o large enterprises: big data for quality management, Long-term cost and time savings, Increased customer satisfaction, Reliable data, Improved productivity.  The use of big data in quality management is the most important "pro" for the implementation of Q4.0 (regardless of the size and nature of the organisation).  Increased accuracy and fewer errors, and better decision making are important "pros" for services, but not for services.  The use of big data in QA, improved productivity and reliable data are the three main "pros" shared by large companies and SMEs.  For SMEs, costs and time savings in the long run are not as significant pros as for large companies.  Five main "cons" for implementing Q4.0: 86 o Manufacturing: High cost of implementation, Lack of financial resources, Lack of knowledge, Organisational culture, Lack of top management support. o Services: High cost of implementation, Lack of knowledge, Lack of top management support, Lack of financial resources, Intensive staff training o SMEs: High cost of implementation, Lack of top management support, Lack of knowledge, Lack of financial resources, Organisational culture o Large enterprise: High implementation cost, Lack of financial resources, Lack of knowledge, Organisational culture, Lack of top management support  The "cons" shared by manufacturing and service organisations are: high implementation costs, lack of financial resources and lack of knowledge.  Training is an issue for service companies, but not for manufacturing companies.  The top five cons of adopting Quality 4.0 were identical between large and SMEs.  Training of people did not seem to be a disadvantage for SMEs and large companies, as the transformation of organisations through Q4.0 would not be possible without intensive training of employees at all levels.  Readiness factors for the implementation of Q4.0: o Overall assessment: Top management support, Leadership, Organisational culture, Customer readiness, Knowledge and awareness, Quality 4.0 vision and strategy, Supplier readiness, Training, and rewards. o Manufacturing: Top management support, Leadership, Organisational culture, Knowledge and awareness, Customer readiness, Quality 4.0 vision and strategy, Supplier readiness, Training, and rewards o Services: Leadership, Senior management support, Customer readiness, Organisational culture, Knowledge and awareness, Quality 4.0 vision and strategy, Training and rewards, Supplier readiness o Large enterprises: Top management support, Leadership, Organisational culture, Customer readiness, Quality 4.0 vision and strategy, Knowledge and awareness, Supplier readiness, Training, and rewards o SMEs: Knowledge and Awareness, Organisational Culture, Leadership, Top Management Support, Customer Readiness, Supplier Readiness, Quality 4.0 Vision and Strategy, Training and Rewards  The three main drivers of readiness for Q4.0 are: top management commitment, leadership, and organisational culture.  SMEs do not have adequate budget and resources in the implementation of Quality 4.0 and do not have a reward/recognition system for employees who carry out projects of this nature.  There are significant differences between large companies and SMEs in four readiness factors: leadership, management commitment, training and reward system and Quality 4.0 vision and strategy.  There are significant differences between manufacturing and service companies in two readiness factors: leadership and training and reward/recognition system.  Leadership is the most significant factor to consider for the successful adoption of Quality 4.0. If leaders do not support and demonstrate their commitment to the Quality 4.0 initiative, the likelihood of failure can be relatively high and will be a waste of time, cost and energy for many people involved in the journey. 5. Antony, Swarnakar, Sony, McDermott & Jayaraman (2023)  How do operational, financial, environmental, and social returns vary between early adopters and late adopters of Q4.0?  Qualitative research: Grounded theory approach.  Participants: 15 managers from different industries and countries (Asia, Europe and USA). Data processing: Atlas Ti9  Impact of early adapters of Q4.0 (before 2019) on performance/performance: o Operational: Increased quality, flexibility, productivity and defect-free products or services; Strength to remain competitive in the long term; Difficulty in getting trainers and skilled staff; Ability to solve quality-related problems ahead of competitors; Implementing Q4.0 requires significant effort and investment; Higher operational and implementation cost; Change management easier o Financial: Meeting customer demands ahead of competitors; Increased sales and revenues; Increased profits and competitive advantage; Reduced cost of sales and inventory cost; Reduced inventory; Minimisation of non-value-added processes; Reduced cost of failure o Environmental: Improved efficiency and optimisation of resources; Minimisation of environmental impact (reduction of waste, scrap, and reprocessing); Compliance with environmental regulations; Secure data related to environmental management and ability to share data in real time; Improved image and loyalty of the organisation o Social: Reduction of accidents; Improvement of occupational health and safety; New jobs; Reduction of monotonous, dangerous, and repetitive work; Reduction of challenges related to HR, government regulations, training, and communications gaps; improvement of peop le´s standards of living; increase of digital literacy in the country. 87 o  Impact of late adapters of Q4.0 (post 2019) on performance/performance: o Operational: Better quality in a short time; More chance of losing customers; Benefit from a mature model with minimal investment and effort; Difficult to get qualified staff; Easier to find qualified trainers; Lower operational and implementation costs. o Financial: Possibility to obtain machines, tools, etc., at a cheaper price than the pioneers; Benefit from updated systems and technologies o Environmental: Reduced pollution through the adoption of cleaner technologies; Benefits of adopting the Circular Economy concept o Social: Safer working environments; Knowledge base o Experienced human resources 6. Canby & Akman (2023)  Analyse the changes in TQM principles (with reference to the ISO 9000 QA principles) in the context of I4.0?  Comprehensive literature review  Questionnaire  Participants: 51 managers of international technology companies.  Data processing: Factor analysis, reliability analysis, correlations, simple regression and DEMATEL method.  TQM factors according to ISO 9000: Customer focus, Top management support and leadership, Employee involvement, Process approach, Continuous improvement, Evidence-based decision making, Relationship management.  Cause criteria: Top management support and leadership, Customer focus and continuous improvement.  Effect criteria: Relationship management, Process approach, Employee involvement and Evidence-based decision making.  When implementing TQM in the context of I4.0, priority should be given to the cause criteria, as these criteria influence the effect criteria.  Top management support and leadership is the most influential factor in the evaluation of changes in TQM principles in the context of I4.0. 7. Liu, Liu, Gu & Yang (2023)  Discover current research trends.  Identify key research topics.  Propose areas for future research  Systematic literature review: 46 articles  Database: Scopus (201 -2022)  Main research topics in Q4.0: (1) Quality 4.0 concept; (2) Implementation of Q4.0 (motivations, barriers, and factors of implementation of Q4.0 in an organization); (3) KM in I4.0 (methods or activities of KM in the I4.0 environment); and (4) Q4.0 model and application (Q4.0 models and cases of application of new technologies)  Proposal for a four-dimensional framework for Q4.0: o 1) Digitization of quality management: using digital tools to improve an organization's ability to meet customer expectations  Managing Q4.0 means digitizing design, compliance, and performance quality, using advanced techniques o 2) Predictive quality management: quality data is automatically collected and analysed in real-time, resulting in "just-in-time" KM  New technologies and analytics make it possible to predict errors in products and processes in advance  Predictive quality management, not corrective or preventive o 3) Mass customization: Thanks to I4.0, customers will participate in the manufacturing process by providing information before, during, and after. Based on big data analytics, a company can understand customer needs, predict market demand in advance, and thus offer the right products at the right time o 4) Smart quality management: In smart factories, all kinds of data associated with raw materials, work-in-progress, and finished products can be identified, monitored, and collected.  Companies can apply analytics to forecasting quality issues and maintenance needs.  Q4.0 Curve Theory: The use of new manufacturing technologies in Q4.0 makes manufacturing quality guaranteed  The focus of Q4.0 is on design quality and service quality, which will have a greater impact on customer satisfaction and competitiveness. 8. Maganga & Taifa (2023)  Seek a universal definition of Q4.0  Identify the factors that facilitate, motivate, and prepare the organisation for the adoption of Q4.0  Identify potential benefits of I4.0 technologies for Q4.0  Bibliometric analysis and visual analysis  Databases: Scopus, WoS, Google Scholar, ScienceDirect and Taylor and Francis  Definition of Q4.0: The bibliometric analysis found no clear definition of Q4.0. However, several authors have defined C4.0, linking it to digitisation, integration, I4.0 technologies and big data management.  Inevitable enablers and readiness factors for Q4.0: new technologies, big data capabilities, skilled and competent workers, and collaboration and leadership support.  Benefits of applying new technologies for Q4.0: o Real-time data collection, remote monitoring, and better visualisation of data. o Integration of machines, processes, people, and the entire supply chain with the help of CFS, IoT or connectivity o Replacing the workforce with autonomous robots to get the job done more efficiently and effectively. o Predictive maintenance through machine learning and AI  reduction of failures, improvement of quality control, increase of productivity and reduction of production costs 9. Mittal, Sachan, Kumar, Vardhan, Verma, Kaswan & Garza-Reyes (2023)  Exploring the organisational variables essential for the successful implementation of Q4.0 in the Indian furniture industry.  Literature review and expert opinions  Analytic Hierarchy Process (AHP)  Essential organisational variables to successfully implement Q4.0: o 1) Digitalisation: Automation, Digital twins, Sensor technology, Cloud computing, Block-chain. o 2) Design: Digital Prototyping, Simulation Modelling, Rapid Iteration, Data-driven Design o 3) Continuous Improvement: Data Analytics, Process Optimisation, Root Cause Analysis, Agile Methodologies (Kanban, Lean) 88 o 4) Employee training and development: Training in digital skills, quality management and change management, Continuous skills upgrading, Digital literacy 10. Prashar (2023)  Review the literature on Q4.0  Identify future research possibilities  Literature review: 51 articles  Databases: Scopus, Web of Science (WoS), EBSCO and ProQuest.  Morphological analysis (proposal of lines of research).  Dimensions of Q4.0: o Production and operations function: influence of the I4.0 environment on QA principles, practices, and tools. o Scope of QA activities: nature and scope of the QA activities examined in the context of I4.0 o Enabling technologies: type of technology used in the literature to operationalise Q4.0 o Digitisation capabilities: organisational and management competences to achieve the objectives of quality control, assurance, and improvement through digitisation of the QA function. o Performance metrics: measures adopted to assess the performance of organisational digitisation initiatives in the context of QA. o QA principles: QA principles adopted for the establishment of a QA system.  Proposed 376 research gaps through morphological analysis 11. Saihi, Awad & Ben-Daya (2023)  What is the impact of I4.0 on improving QA practices and how can new technologies be harnessed for the benefit of QA?  What are the current challenges hindering the adoption of I4.0?  Systematic literature review: 52 articles (2016-2020)  Databases: Scopus, Engineering Village databases and Google Scholar  Impact of I4.0 on Quality Practices: o Inspection and Quality Control: Obtaining information in real time; Faster management of advanced tasks; Automatic, continuous, real-time monitoring and alerting; New types of data and metrics available in real-time; Availability of new inspection techniques and tools; Early detection of failures and self-adaptation techniques; Identification of quality-related causes and adjustment of processes without delay; Greater adjustability; Detect new types of defects that were not possible before; Quality control can be carried out instantaneously on the production line and a differentiated metrology section is no longer necessary; 100% recording of data and guarantee of 100% control; Possibility of "on-site" correction of the process (correcting the change in quality during the production itself; Improved data-driven decision-making using big data analytics and cloud solutions; Augmented reality technology can be used in inspection by displaying graphical instructions and tutoring the user on the inspection procedure; AI techniques support visual inspection; Model training on possible out-of-control situations using Machine Learning (ML) techniques; Recommendation of corrective actions using AI and ML techniques o Quality Planning: Use of prescriptive analytics algorithms to determine the best solutions and offer recommendations; Customer specifications can be collected thanks to the end-to-end integration facilitated by I4.0; Data can be automatically transmitted from customers to the design department in terms of specifications and quality features; Gain insight into customer experience and product performance in the field; Improve new versions of the product based on their tracking during the usage cycle o Quality Assurance: Use new technologies to implement new QMS documentation systems; Adopt new ways of developing, implementing, and maintaining QMS documentation; Automate the document control process. o Continuous improvement: Fully integrated production systems that provide a foundation for CI at the product, process, and business performance levels; Contribute to the improvement of future products using the data collected on product usage, performance, and issues; Provide recommendations for appropriate correction, encouraging continuous improvement and future innovations; Use advanced data analytics to make faster, more reliable decisions for quality improvement. o Predictive quality: Prediction of defects/quality and early diagnosis of equipment failures; Process improvement facilitated by data-driven quality prediction; Automatic prediction of failures and connected self-learning machines capable of making decisions; Defect prediction and performance improvement; Error prediction and continuous optimization; Predict at an early stage whether the final product will meet specifications.  Future lines of research: o There is a lack of clear evidence on how I4.0 can be used for KM o There is a lack of case studies that provide an overview of the real-world use scenarios of I4.0 in the field of quality. o Few studies attempted to define Q4.0 and their studies do not include a comprehensive theoretical definition. o Researchers have focused on the potential benefits offered by I4.0 in quality inspection and control and process control, while less attention has been paid to the contributions to quality assurance and quality planning 12. Schiavone, Pietronudo, Sabetta & Ferretti (2023)  How do total quality service (TQS) practices change in digital-based service organisations?  Case study  Dimensions of the theoretical model of digital SCT: Top management commitment and visionary leadership; Human resource management; Technical system and information and analysis system; Service culture; Service environment; Social and environmental responsibility; Networking; Continuous improvement; Customer orientation and employee satisfaction.  Proposals: o P1. Transformational and transactional leadership fosters TQS practices in digital environments. o P2. Continuous empowerment is a condition for stimulating TQS in digital environments: Continuous empowerment affects service culture; SCT is a condition for continuous empowerment. o P3. Technical systems and information and analytical systems are closely linked to ensure SCT. 89 o P4. The intangible and virtual elements of the service environment affect SCT, as does the tangible element. o P5. The social responsibility of SCT includes social innovation and data transparency and ethics. o P5.1. Public digital service companies improve SCT by leveraging social innovation. o P6. Networking is a component of SCT for digital service companies 13. Van Nguyen, Tucek & Pham (2023)  Identify the main factors of the TQM 4.0 model and their relative importance.  Identify indicators of the TQM 4.0 model implementation factors.  Delphi techniques: 90 academic experts, practitioners, and consultants with at least 5 years of production or quality management experience and basic knowledge of Industry 4.0 tools.  Analytical Hierarchical Process Analysis (AHP)  Classification of TQM 4.0 key factors and their indicators: o 1. Top management: Commitment of top management, Involvement of top management, Contribution of resources by top management, Establishment of policies, objectives, and indicators by senior management o 2. Quality Culture: Quality-Oriented Mindset, Employee Training, Individuals Who Understand Their Role in Achieving Quality Goals, Quality Articulation o 3. Competency 4.0: Skills related to analytics, AI, SPI, Digital skills for quality staff, Digital communication skills, Team creativity skills. o 4. Smart Organization: Senior Management Support Initiatives, Dissemination of Organizational Knowledge, Lean Structure Organization, Collaboration of All Stakeholders, Adaptability in a Rapidly Changing Environment, Application of Online Tools in Training, Meetings, and Work Management. o 5. Integration of sustainable development: Linking quality and sustainability, Companies at the service of society, Operations in a more sustainable way, Integration of environmental management systems o 6. Automated Document Control: Integration of documentation into ERP modules and automatic review, electronic documentation, Real-time document control, Digital Standard Operating Procedures (SOPs) o 7. Automatic Data Collection: Automatic data collection throughout the product lifecycle, Automatic collection of many types of product-related data, Automatic collection of many types of customer-related data. o 8. Intelligent Quality Control: Real-Time Quality Inspection, Total Inspection, A New Type of Machine Learning-Based SPC, Data Integration with Enterprise Resource Planning o 9. Intelligent Quality Assurance: Using AI Software for Prediction and Prevention, Using Sensors at Every Production Stage, Big Data Analysis, Making Smart Adjustments, Improving Machine Performance Using ML o 10. Smart Product: Market Demand Prediction and Consumer Trends, Intelligent Identification and Traceability Technologies, RFID Technologies and Smart Sensors, Customer Involvement in the Production Process. 14. Zulfiqar, Antony, Swarnakar, Sony, Jayaraman & McDermott (2023)  What are the factors for assessing the readiness to implement Quality 4.0 in the packaging industry?  How to assess the readiness of the packaging industry to implement Quality 4.0?  Qualitative (literature review and expert interviews) and quantitative (online surveys) study.  Participants: 45 general managers and heads of departments of 6 Pakistani companies in the packaging industry.  Data processing: analysis of variance (ANOVA)  Q4.0 readiness factors (packaging industries): Top management commitment and support; Leadership; Organisational culture; Employee competence; Implementation of quality management systems.  Readiness assessment factors are classified into three levels: "Risk level", "Acceptable level" and "Safe level".  Organisational readiness is classified into five levels: "Chaotic organisation", "Primitive organisation", "Structured organisation", "Mature organisation" and "Competent organisation".  Final assessment of readiness for Quality 4.0: o Mature level (C, E, F): employees in these types of organisations have sufficient knowledge about quality tools and strategies. However, some barriers prevent this type of organisation from reaching competence. o Structured level (A, B, D): Employees are more involved in quality management practices and resources for quality improvement are provided by top management. Top management is more quality conscious but does not calculate the cost of quality. This type of organisation is more involved in quality practices and willing to embrace quality. o Since Quality 4.0 is at an early stage, and although there are some early adopters, many organisations have not yet reached the proficient level of Q4.0 implementation. 15. Ali & Johl (2022)  Identify the most relevant issues and problems in the literature on Q4.0.  Systematic literature review  Database: WoS and Scopus  87 empirical studies (1987 and 2019)  Pareto analysis  Identification of 23 soft and 15 hard TQM practices relevant to the implementation of Q4.0 in the manufacturing and service sectors.  Most relevant soft dimensions for implementing Q4.0 (vital few): Top management commitment, Customer orientation, Supplier and partner management, Training and learning, Strategic quality planning, Quality information and analysis, Human resource management (recognition, teamwork), Employee involvement.  Most important hard dimensions in the implementation of Q4.0 (vital few): Product and process management and control, Continuous improvement, Product/service design, Benchmarking, Cross - functional product design, Feedback 16. Antony, McDermott & Sony (2022)  What are the motivations and benefits of adopting Q4.0?  What are the critical success factors, challenges, and  Qualitative study (literature review and expert interviews).  Participants: 12 senior managers (with C4.0 experience) from manufacturing, services, small and  Motivations: eliminate human inspection, improve process control, exploit synergies with Lean or Six Sigma, improve performance and competitiveness, prepare the organisation for the future, better data management, support management and training.  Benefits: use of big data, improved data analysis, customer satisfaction, visual inspection, immediate data retrieval, automated quality checks, waste reduction, error detection, predictive analytics, synergies with Lean, elimination of paper, real-time data capture, process improvement, improved decision - making. 96 Online Surveys 3 6,98 27,91 Grounded theory approach 1 2,33 30,24 Comprehensive Literature Review 1 2,33 32,57 Systematic Literature Review (SLR) 15 34,88 67,45 Bibliometric Analysis 2 4,64 72,09 Visual Analysis 1 2,33 74,42 Expert Opinions 1 2,33 76,75 Case Study 1 2,33 79,08 Delphi Techniques 1 2,33 81,41 Content Analysis 1 2,33 83,74 Systems Thinking 1 2,33 86,07 Conceptual Approach 1 2,33 88,40 Framework and Methodology Develop. 1 2,33 90,73 Expert Input 1 2,33 93,06 Narrative Literature Review 3 6,98 100 Total 43 * 100 *There are studies that use more than one type of analysis. Table 10. Type of instruments for obtaining the data used. Source: Owner. Types of quantitative analyses Frequency Relative Freq. (%) Accumulated Freq. (%) Frequencies 1 5 5 Percentages 1 5 10 T-tests 2 10 20 Regression analysis 1 5 25 Factor analysis 1 5 30 Reliability analysis 1 5 35 Correlations 1 5 40 Simple regression 1 5 45 DEMATEL method 1 5 50 Analytic Hierarchy Process (AHP) 3 15 65 Morphological analysis 1 5 70 Analyses of Variance (ANOVA) 1 5 75 PARETO Analysis 1 5 80 ATLAS Ti9 1 5 85 MS Excell 2 10 95 R-Student 1 5 100 Total 20* 100 * There are quantitative studies that use more than one type of analysis, and not all of studies use quantitative method. Table 11. Types of quantitative analyses used. Source: Owner. 97 Sectors Frequency Relative Freq. (%) Accumulated Freq. (%) General / Several 20 64,62 64,62 Manufacturing 6 19,35 83,97 Services 3 9,68 93,65 IT 2 6,45 100 Total 31* 100 * There are quantitative studies that use more than one type of analysed activity. Table 12. Types of sectors of activity analysed. Source: Owner. According to Suárez et al. (2017), the quantitative method used will be very much related to the objective of the work and the data type available to the researcher. In addition, the great majority of articles are transverse in nature. So, and observing the results of the tables presented before, it´s possible to confirm on table 10, in this analysis that Systematic Literature Review (SLR) was the most used instrument by the authors as quantitative method to extract fundamental data for the research, also with interviews and questionnaires, with 6,98%, 13,95%, 34,88% of total of used instruments, respectively. As type of quantitative analyses used, also it could be observed on table 11 that the Analytic Hierarchy Process (AHP) was the quantitative analyses type most used with 15% of the total of the types used by the authors on its researches. Also, T-tests was used with 10% of the total of quantitative analysis types. Finaly, the multi-sectorial studies stand out on table 12, as the most types of the sectors of activity analysed with 64,62% of the total of the analysed sectors by the authors of the studies. Forward, on the next section will take a conclusion about what was presented before on the SLR quantitative research analysis and try to indicate some possibles limitations of this research, as like motivations and barriers for the Quality 4.0 implementation, also its challenges. 3.4.6 Conclusions and limitations Through the Systematic Literature Review (SLR), we were able to formulate the problem to be addressed in this investigation, define the criteria for including and excluding articles for analysis, search for and select these articles, present the research findings from the articles for study, and quantitatively analyze this SLR using selected instruments, all to obtain data for analysis. As a result of the SLR, we reached the following conclusions: 1. There is a lack of consensus on the key factors needed for implementing Q4.0 in Organizations; 2. There is a scarcity of empirical research that analyzes which factors are necessary and sufficient for the implementation of Q4.0 in Organizations; 3. Different combinations of key factors can lead to organizational readiness for the implementation of Q4.0, or there may be a lack of preparation altogether. 98 Just was mentioned before, the main objective of this SLR quantitative research is to help us to find the literature support to justify this research. According to Sanchez-Meca (2010) and corroborate by Suárez et al. (2017), is to allow us to make a search, filtration, and selection of and to the research, the main literature for later analysis. That analysis will take place on the methodology chapter, with the combination of the (1) the Partial Least Squares Structural Equation Modelling (PLS-SEM) technique to estimate sufficient conditions; (2) the Necessary Condition Analysis (NCA) technique to identify the necessary causality; (3) and the Generalized Analytic Induction (GAI) to identify the combination of conditions that explain the presence of a result. A several literatures exists in this kind of study area, but not even or specifically aboard this study proposal. So, it was with the literature selection that we could built our constructs, fundamental to stablish the hypothesis will be presented in next section and could be tested and answer to the research question. But for now, it’s important to take some considerations about was the SLR quantitative research analysis, and to indicate some possibles limitations of this research, as like motivations and barriers for the Quality 4.0 implementation, also its challenges: 3.4.6.1 Motivations and barriers for Quality 4.0 implementation According to the research constructs, Sony et al. (2021) after collecting feedback from a group of quality management experts and practitioners identified the main motivations and barriers to implementing Quality 4.0 in organisations (Figure 14). Regarding motivations, the main ones pointed out by experts and professionals are as follows: (1) Obtaining reliable and accurate data for quality management. Decisions must be based on accurate measurements and not on the intuition or beliefs of decision-makers. Quality decisions are more efficient when they are based on solid information drawn from reality. In quality management it is often said: if you want something done, measure it; what you cannot or will not measure is very difficult to improve. (2) Integration of Big Data into quality management programmes. Big data can help QA to obtain a large amount of relevant strategic and operational information from departments, supply chains or customers. Having this knowledge is essential to be able to react quickly to changes in today's environment. (3) Improving customer satisfaction. Customer satisfaction is achieved when products and services meet their needs and expectations. Thus, Quality 4.0 through the advantages of digitalisation and Big Data can gain quick and reliable insight into the impact of products and services on the market and how they are meeting customer needs and expectations. 99 (4) Productivity improvement. As noted above, Industry 4.0 involves jointly optimising technical (automation, digitisation, and data) and social (leadership, people, and culture) systems, which will improve the overall productivity of the organisation. (5) Time and cost savings in the long term. The investment in technology, automation or training will be high for companies implementing Quality 4.0. However, in the long run it will reduce the costs of internal and external failures, i.e., the costs of poor quality will be reduced. On the other hand, quality costs related to detection or inspection (data collection, verification, or analysis) can also be reduced. Therefore, in the medium and long term, it is possible to save costs and time with the implementation of Quality 4.0. Figure 14. Motivations and barriers to the implementation of Quality 4.0. Source: Adapted from Sony et al. (2021). The main barriers identified by the Sony et al. (2021) study are: (1) Cost of implementing Quality 4.0. Investment in technology and digitalisation can be associated with excessive payback time. As Quality 4.0 is a new concept, it is not clear to organisations how to estimate the return on investment brought about by Quality 4.0 initiatives. (2) Lack of resources. Implementing Quality 4.0 requires physical and technological resources such as CPS, IoT, sensors or smart manufacturing; intellectual resources such as know-how, databases, or smart algorithms; human resources such as qualified quality managers, quality engineers or quality managers; and financial resources. (3) Lack of knowledge on how to implement Quality 4.0. There is no universally accepted framework for implementing Quality 4.0 (Zonnenshain and Kenett, 2020). The knowledge base 100 of Quality 4.0 in terms of implementation framework should be standardised so that an organisation finds it easier to implement for organisations. (4) Absence of an organisational culture conducive to Quality 4.0. Not all organisations have an organisational culture conducive to the implementation of Industry 4.0 and Quality 4.0. In this sense, a culture that fosters knowledge creation and transfer, innovation and creativity is necessary. In such a culture, an organisation creates an environment that encourages employees to take risks, accept new challenges and support creative work to meet quality objectives. (5) Not considering Quality 4.0 as a source of competitive advantage. In the era of the Fourth Industrial Revolution, one of the challenges organisations faces is finding the right way to shape their competitive advantage. This is important, as it paves the way for long-term survival in the market. The nature of the relationship between the implementation of Quality 4.0 and competitive advantage is unclear and there is no solid evidence. 3.4.6.2 Quality 4.0 Challenges According to the research constructs. the study by Küpper et al. (2019) identified a shortage of digital skills, an unclear digital strategy, and a lack of quality culture as the top three challenges for companies in successfully implementing Quality 4.0 initiatives. Other important issues included outdated technology systems and infrastructure, lack of resources, resistance to change, lack of top management support, data integrity and quality, and cybersecurity. Along these lines, Sader et al. (2019) recognises the opportunities that Industry 4.0 and its technologies offer to quality management but highlights that there are also ambiguous areas (threats and challenges) within this context. For example, there are difficulties related to know-how and organisational barriers that hinder the implementation of Industry 4.0 in small and medium-sized enterprises. On the other hand, although process automation can improve connectivity between customers and organisations, there are some tasks that are irreplaceable because they require face-to-face interaction. For example, dealing with customers before, during and after the sale could be one such task. The same can be true for key quality management activities such as leadership, process approach and decision making. In these activities, people's experience, skills, and qualifications are needed to reach satisfactory conclusions from the data analysis and information provided by Industry 4.0 technologies. In addition, cyber security and data protection is also one of the most important challenges of Industry 4.0, i.e., how to maintain the flow of information related to quality management while avoiding data loss or inaccuracy. Finally, it is also important to define a set of specific quantitative measures as evidence to describe the real impact of Industry 4.0 on quality practices, e.g., what is the quantitative change resulting from implementing Industry 4.0 in an organisation in terms of customer satisfaction, product and process improvement, cost of quality, efficiency and effectiveness of processes and leadership, decision making process and finally in improving the overall results? 101 For Sader et al. (2022) the main challenges of Quality 4.0 are related to human resources, organisational, technological, and managerial aspects. Moreover, these challenges can also be considered as success factors, as qualified human resources, good organisational performance during the change required for the implementation of Quality 4.0 and organisational readiness from a technological point of view will lead to a successful transition from traditional quality management systems to Quality 4.0. 1. Challenges related to human resources. For Aldag and Eker (2018) the implementation of Quality 4.0 will lead to higher performance because of the automation of quality inspection and analysis activities. Therefore, future uses and activities related to quality management will require more sophisticated talents and higher qualifications and competences to understand new technical opportunities and how to apply them (Sader et al., 2022). 2. Organisational challenges (leading the change). To have a successful transition from traditional quality management to Quality 4.0, quality teams and especially quality managers must be fully aware and understand the new technologies of Industry 4.0 and their impact on quality and must play a leading role in the transition process. Furthermore, the success of Quality 4.0 cannot be achieved without a solid foundation of traditional quality management, i.e., Quality 4.0 does not replace quality management principles, but rather enhances them. 3. Challenges from the technological and management perspective. The technological challenges for Quality 4.0 concern aspects related to infrastructures, the selection of appropriate technologies and the type of data collected and how it can be analysed and linked together. Undoubtedly the successful implementation of Quality 4.0 requires reliable data sources and advanced analytical tools that can collect accurate information, such as customer feedback on social media, and impose the right actions based on that real data (Sader et al., 2022). In addition to the aforementioned challenges, Quality 4.0 is also affected by Industry 4.0 challenges, such as security or reliability of networks and devices, (Lu, 2017). According to Fadilasari et al. (2024), the main obstacles to the adoption of Quality Management Practices (QMP) or Q 4.0 in the face of digital innovation are "Organizational Behavior" and "Information Technology and Governance." These are identified as critical dimensions that create a complex landscape for stakeholders. At the core of these challenges are leadership, cybersecurity, data protection, and organizational structure, which are highlighted as the primary areas requiring strategic focus. Ultimately, companies that succeed in mitigating or alleviating the challenges of the Quality 4.0 transformation can gain competitive advantages and achieve greater customer satisfaction and business success (Sader et al., 2022). 102 3.4.6.3 Limitations The articles used in this study have some limitations due to the methodology adopted, which excluded certain relevant research. Firstly, only articles published in journals with a CiteSource, SJR, and SNIP impact index, as well as those present only in the Scopus database, were considered, which considerably reduces the search and search reach if other engines had been used simultaneously. Secondly, the study did not include articles written in languages of countries that have a long history of implementing TQM 4.0, Quality 4.0, or Management, such as French, German, Spanish, or Portuguese. Thirdly, the study focused on quantitative investigation, while literature of a qualitative and mixed nature also provides crucial insights into the field of TQM 4.0, Quality 4.0, or Management. Finally, and according to Massaro et al. (2016) it's important to note that Systematic Literature Reviews (SLRs) are not a cure-all or the end of research but should be viewed as the beginning of new journeys. Therefore, the objective of this study is not only to summarize existing knowledge but also to identify knowledge gaps and offer research routes for future studies. 103 4. RESEARCH MODEL AND HYPOTHESIS After the SLR quantitative research was done in the previous section, we are now in conditions to present and propose a model based on in some hypothesis. So, just we´ve mentioned before, the goals for this quality 4.0 research collection were well identified, considering the facing reality, also identified, the motivations and barriers which organizations has when desire to implant a Quality 4.0 organizational system. For that it is crucial to identify which readiness factors an organization should consider before implementing and applying Quality 4.0-related projects (Antony et al., 2023b). As Kumar et al. (2022) indicate, quality management in Industry 4.0 is still nascent, and researchers are trying to identify critical success factors. While most research focuses on the factors that produce specific outcomes, those that facilitate outcomes are equally important for decision-making (Van der Valk et al., 2016). In this sense, no research in the Q4.0 literature addresses the identification of must-have and should-have conditions. In this study, we fill this gap. To do so, we reference the readiness factors for Quality 4.0 proposed by Antony et al. (2023b). So, and according to the identified goals, we’ll used as methodology four techniques. Taking our population sample, we’ll apply (1) “The Partial Least Squares Structural Equation Modelling (PLSSEM)” technique estimates sufficient conditions (Hair et al. 2019a) to Identify sufficient factors to prepare an organization to implement Quality 4.0, where these factors are analysed in terms of sufficient causality, (2) “The Necessary Condition Analysis (NCA) technique (Dul et al., 2023)” to Identify the factors necessary to prepare an organization for Quality 4.0, where in this case, a necessary causality implies that without the condition, the result will not occur, although its presence does not guarantee the result, and (3) the Generalized Analytic Induction (GAI) to identify the combination of conditions that explain the presence of a result or outcome will be identified (Ragin, 2023). These types of factors represent barriers or obstacles, i.e., factors that others cannot compensate (Dul et al., 2020; Richter et al., 2020). To complete both analyses, two techniques based on set theory will be applied in the results area (chapter results) to identify both the sufficient conditions that are linked to the first specific objective and the identification of necessary conditions linked to the second objective. While (4) Qualitative Comparative Analysis (QCA) identifies both types of conditions, Generalized Analytic Induction (GAI) identifies sufficient conditions. As results and conclusions in this section, and after applied this model to the identified goals, it will be possible to response to the first objective, it is found that Quality 4.0 vision and strategy, Customer readiness, Human resource management for Quality 4.0, Leadership and top management commitment and Organizational culture towards Quality 4.0 are sufficient factors for organizations to be ready to implement Quality 4.0. About the second research objective, it is confirmed that Quality 4.0 vision and strategy and Supplier readiness are necessary conditions for 104 Readiness for Quality 4.0. Thus, unless Quality 4.0 vision and strategy and Supplier readiness reach a certain level, Readiness for Quality 4.0 cannot occur. This could be proved on the followed methodology chapter and next, where it will be developed and mentioned by relevant authors which aboard the subject of the key factors for the Quality 4.0 Organizational implementation, and the Key Drivers, Barriers, and Challenges for the Implementation of Quality 4.0, as it mentioned before on the previous section. For that, it is in this section that it will forward and proceed the aim of this study and try to present, according to the relevant literature and the author understanding, what are the sufficient factors for organizations to be ready to implement Quality 4.0 and what are the necessary conditions for Readiness for Quality 4.0 implementation, trying to answer to the central questions research and proposal objectives, also mentioned on the sections before. The analysis of Q4.0 readiness factors in the literature has been carried out through different approaches and methodologies. Sony et al. (2020) conducted a narrative literature review in which they selected studies of a qualitative and quantitative nature, as well as journal and conference proceedings. As a result, they identified eight key ingredients for the effective implementation of Q4.0: Big data; Prescriptive analytics; Integration; Q4.0 for strategic advantage; Leadership in Q4.0; Training in Q4.0; Organisational culture for Q4.0 and Top management support. It should be noted that, in this case, the authors do not speak of readiness factors but of essential ingredients. Sony et al. (2021), following a review of the literature, propose eight readiness factors: Leadership; Top management support; Q4.0 vision and strategy; Training and reward; Knowledge and awareness of Q4.0; Organisational culture towards Q4.0; Customer and Supplier centredness. These factors are then ranked according to their importance through an online survey of quality professionals. Antony et al. (2022; 2023a) initially used a qualitative approach based on interviews with managers of companies that were implementing Q4.0 to identify a series of readiness factors, as well as the primary motivations, benefits, or barriers to the implementation of Q4.0. Specifically, they recognised readiness factors such as employee training and knowledge of data analytics and Big Data techniques, cultural transformation, deployment of a Q4.0 vision and strategy, awareness of Q4.0 among customers and suppliers, and effective management of resistance to change. Next, Antony et al. (2023b) developed a quantitative study to examine Q4.0 readiness factors in a sample of industrial and service organisations in the Americas, Asia, and Europe. To obtain the data, the authors designed an online questionnaire to propose measurement scales for each factor identified by Sony et al. (2021). In short, the Q4.0 readiness factors identified in the literature represent a multidimensional phenomenon (Sony et al., 2021) and correspond mainly to what Sureshchandar (2022) calls "traditional elements" of quality management, although adapted to the new context represented by I4.0. Thus, we find factors such as the leadership and commitment of top management, the strategic vision of Q4.0, aspects related 105 to human resources (commitment, training, skills, or rewards), and the organisational culture or the involvement of customers and suppliers with Q4.0. Therefore, Q4.0 is much more than technology and involves factors of a social nature, such as management leadership, human resources, and organisational culture, as well as strategic factors, such as the consideration of Q4.0 as a core component of the organisation's strategy and the involvement of customers and suppliers (Küpper et al., 2019; Sony et al., 2021; Antony et al., 2023). In the following, we attempt to justify the research model's hypotheses that address these social and strategic factors (Figure 15). Figure 15. Research model and hypotheses. Source: Own elaboration. 4.1 Leadership and top management commitment The level of top management's understanding of the significance of Q4.0 and willingness to support its implementation inside the organisation is called leadership and management commitment (Sony et al., 2021). Sureshchandar (2022) indicate how any quality management initiative considers leadership a critical success factor. On the other hand, Sony et al. (2020) argue that many quality initiatives fail due to lacking top management commitment. For Sony and Aithal (2020), leadership is one of the most critical aspects in guiding the process of adopting technology and digitisation, as it is a radical change in the organisation. Antony et al. (2023b) considers that one of the most challenging aspects of implementing Q4.0 is the changes that need to be introduced in the organisation, which some of its members may need help to achieve. However, with management commitment, these changes will likely be managed successfully. Furthermore, the change brought about by Q4.0 affects internal processes and the entire supply chain (Antony et al., 2022). In short, leaders and senior managers must act as role models of a quality culture, must be personally involved in ensuring the development, implementation, and continuous improvement of quality, must Readiness for Quality 4.0 (RQ) Supplier readiness (SR) Customer readiness (CR) Organisational culture towards Quality 4.0 (OC) Leadership and top management commitment (LD) Human resource management for Quality 4.0 (HR) Quality 4.0 vision and strategy (VS) H 1 + H 2 + H 3 + H 4 + H 5 + H 6 + 112 It is also important to bear in mind that the research topic is not static, but dynamic, i.e., it has evolved and is evolving as the research and data collection progresses. This means that the author, based on his central research question and throughout his period of work, has been adapting and updating the "state of the art" in relation to the same central question and research object of this thesis, based on the most up-to-date literature that has been and is being published about study. To define our sample, the population under study will constitute the number of companies that currently belong to the Club of Excellence of the European Foundation for Quality Management (EFQM) in Spain, whose members have a distinction called the European Seal of Excellence, since the focus and object of study was initially the universe of companies and organisations certified with the EFQM Iberian seal (Portugal and Spain). The choice of only Spanish companies was justified by the low number of responses to the questionnaires submitted throughout the data collection process, which made our analysis difficult. This decision was naturally made because the contact details of the companies in the EFQM Portugal database were also out of date, which naturally contributed to the almost absent number of responses from companies in this country. As developed in the previous chapter, the author used a specialised systematic literature review (SLR) as a research strategy (quantitative research) in intention to identify and define the "state of the art" regarding this research topic, making it possible to propose an analysis model and present a set of hypotheses to be tested. In this chapter and based on the combined use of Partial Least Squares Structural Equation Modelling (PLS-SEM), Necessary Conditions Analysis (NCA) and Generalised Analytical Induction (GAI) to identify the combination of conditions that explain the presence of an outcome, it will be possible to test and prove the model presented to draw the appropriate conclusions. 5.2 Target sample and total population As it knows, and according to Orero-Blat (2022), the sample is the subgroup of the universe or population from which data are collected and which should be representative of it. Its objective is to select representative cases for the generalization of characteristics and hypotheses with the purpose of constructing and/or testing approaches applicable to the population. In present case, the target audience of this research are the Spanish organisations with recognition of excellence awarded by the European Foundation for Quality Management (EFQM). In the beginning, the idea was making for all Iberian (Portugal and Spain) Organizations but, just like we’ll follow during the data collect process on this chapter, it was very difficult obtain answers from Portugal, so, for that reason was decided by the author to do not include the EFQM Portuguese Organizations in the target population, and consequently in the sample. 113 The EFQM Seal of Excellence recognizes those organizations that have opted for the improvement of their management system, to guarantee optimal development and application of the self-evaluation and external evaluation methodology. Currently the EFQM model includes up to four levels of recognition: European Excellence 300+, 400+, 500+ and the Commitment to Excellence Seal (200+). A noteworthy data is the number of companies whose stamps are +400 and +500 (more than 60% of the organizations), indicating the growing commitment to organizational excellence. Organizations can be classified according to the type of activity, where organizations with an educational purpose and health stand out. Another way of classification could be based on the experience that the company has been implementing the excellence model, that is, if its recognition has increased over the years (to higher categories in Seal). In this case, the author opted, taking into account the analysis techniques used, which we will discuss below, to create segmentation variables, where he constructed a questionnaire based on the hierarchical position and responsibility that the recipient occupies in the company, the type of EFQM recognition that the company has, the size and dimension of the company in the market in relation to the Spanish classification and the sector of activity in which it operates. It also created a variable in which it assigned rating levels to the organisation's readiness to implement quality 4.0. In this way, it seems clear to the author that the EFQM model as a recognized seal was useful in the use and application of the concepts of Industry 4.0 and Quality 4.0, particularly when trying to assess and identify what are the key factors for implementing a quality system 4.0 within an industry 4.0 context in the Spanish business fabric. It is also possible to identify the main barriers to this implementation and thus be able to minimise and balance the costs of change and, once the company is ready to receive a 4.0 quality system, to identify the key factors for implementing this same quality system, also in the context of Industry 4.0, based on a set of hypotheses proposed and to be tested below. The choice of this target population is motivated by the fact that this type of organisation is committed to knowledge, innovation, development, and improvement of capabilities as strategic drivers to achieve competitive advantages in the markets (Tarí et al., 2023). For Bocoya-Maline et al. (2024), the EFQM Model has been recognised as a comprehensive management framework for organisations, regardless of their size, sector, ownership, or scope of operations, which drives change management, innovation, learning, continuous improvement and superior stakeholder performance. In this regard, Nenadál (2020) and Fonseca (2022) argue that the EFQM Model can be a framework to base the successful implementation of Industry 4.0 and Quality 4.0. As it’ll could be seen forward in the next sections by the sample distribution, the total size analysed of that was 107 Spanish EFQM organizations, also from, a 407 total EFQM Spanish organizations population, obtained from validated questionnaires received. 114 5.3 Data Description & Data Collection As an instrument for data collection, a questionnaire was emailed to directors/managers and qualityrelated positions. The first questionnaires were sent out in March 2022, while the last were received in January 2023. In the end, 107 valid questionnaires were obtained, giving a response rate of 26.29%, which is considered acceptable for online surveys (Easterby-Smith et al., 2012). Bellow, the characteristics of the sample are shown in Table 13: Characteristics Frequency Percentage Respondent profile ● Senior Manager 50 46.7 ● Quality Manager 57 53.3 EFQM recognition system ● +200 23 21.5 ● +300 13 12.1 ● +400 30 28 ● +500 29 27.1 ● +600 12 11.3 ● +700 0 0 Sector ● Industry 59 55.1 ● Services 48 44.9 Size < 250 employees (SMEs) 59 55.1 ≥ 250 employees (large company) 48 44.9 Table 13. Sample description characteristics. Source: Owner. According to related bibliography, the sent questionnaire was built considering the segmented variable identified above and our research object. So, in attachment of this thesis could be consult the sent questionnaire in “annex III – Questionnaire on an Orhanization´s readiness for implementation of Quality 4.0”. 5.4 Measurements Once the information has been collected, the researcher will need to select it. Since it is not possible to analyse all the information collected, the researcher will have to select that which is most important and relevant to answering the research question. Thus, it is at this stage that the procedures for analysing the data collected and validated begin, which are determined based on the methodology and techniques selected and applied for this research. In the theoretical chapter of this thesis, the central constructs of this research have been extensively explained, as well as the different ways that have been complied by the academic literature to measure the different constructs, by adapting the innovative protocols to the systematic literature review. that is 115 why in this section we will focus on explaining the measurement scales corresponding to the questionnaire used. Following Antony et al. (2023b), a group of experts and academics with knowledge of Quality 4.0 participated in a pilot test to assess the validity and reliability of the measurement instrument. This pilot test made it possible to check the clarity of the questions, whether essential questions have been omitted or are redundant, or the time needed to answer the questionnaire. Most of the group's comments were positive, although changes were made. The most significant changes concerned the grouping of some Quality 4.0 readiness factors, eliminating redundant questions or items and rewording some confusing questions. The final questionnaire was structured in three blocks (Annex II). In the first block, information on contextual variables was requested. The second block asked about Quality 4.0 readiness factors. These factors and their measurement scales were adapted from the work of Antony et al. (2023b). A 7-point Likert scale was used to measure the items where (1) "Strongly disagree", (4) "Neither agree nor disagree", and (7) "Strongly agree". Finally, in the third block, the respondent was asked about the degree of preparation of his or her organisation for implementing Quality 4.0. In this case, the scale used was a 7-point Likert-type scale where (1) "Very low level", (4) "Medium level", and (7) "Very high level". Factors and organisational readiness for Quality 4.0 were designed in PLS-SEM as Mode A constructs, i.e., reflective variables or constructs (Hair et al., 2021). 5.5 Data Analysis Process The PLS-SEM technique is becoming increasingly popular among social science researchers, particularly in fields like marketing and business management (Hair et al., 2011). This is because composite constructs are often used in research models, and PLS-SEM is well-suited for these types of constructs (Roldan and Sanches-Franco, 2012; Benitez-Amado et al., 2017). PLS-SEM is often preferred for prediction-oriented research, which is the focus of this doctoral thesis and is validated by the PLS methodology (Leal-Rodriguez et al., 2023). According to Bolívar, L.M., et al. (2022), the PLS-SEM (structural equations) use, allow us to see the strength of the relationship between the preparation factors and the degree of maturity in the implementation of Quality / Industry 4.0, i.e., determining and identify what are the Key factors for the Identification / implementation of Quality and Industry 4.0. At the same time, the use of analysis Necessary Condition Analysis (NCA) to identify the necessary antecedent variables in the research model, in our words, this model allows to know which preparation factors are necessary, and which are not to implement Quality / Industry 4.0 (Bolívar, L.M., et al., 2022; Richter, N.F., et al., 2020). 116 Partial least squares are a widely applied variance-based structural equation modelling approach. For some time, it was argued that this technique is suitable for social science research insofar as its assumption’s present fewer restrictions regarding the normality of the variables or the sample size (Hair et al., 2014). The PLS-SEM method has grown in popularity among academics in the field of social science, finding widespread application in industries like marketing and business management (Hair et al., 2011). So, the models or techniques we’ll be used for data analysis in this research are PLS-SEM (structural equations), NCA (Necessary Condition Analysis), GAI (Generalysed Analytic Induction), QCA (Qualitative Comparative Analyses), the combinations of conditions that explain the existence of a given outcome will be identified (Ragin, 2023). For Hair et al. (2019a) the PLS-SEM technique estimates the causal-predictive relationships (sufficient conditions) included in this study. As Tarí et al. (2023) point out, PLS-SEM is increasingly being applied in disciplines such as operations management. More specifically, the reasons for applying PLS-SEM are: (1) this is an exploratory study (Hair et al., 2019b); (2) this study uses a small sample size (Richter et al., 2016); (3) one of the objectives of the study is to test a theoretical framework in terms of prediction (Hair et al., 2019b ), which is an inappropriate research purpose for Covariance-Based Structural Equation Modelling (CB-SEM) (Rigdon, 2012). Accordingly, the research conducted in this doctoral thesis utilized, and was applied by us the Smart PLS 4 software (Ringle et al., 2022). This methodology consists of three steps that were carefully followed to establish the measurement model, structural model, and predictive ability of each research model proposed. The decision to use this methodology was based on the characteristics of the constructs analysed and the research topic's paradigmatic situation. By utilizing this methodology, and the hypotheses tested, the research question will be answered. In the additive logic used in PLS-SEM, the goal is to explain an outcome through various determinants. These individual determinants are sufficient but not necessary to change the outcome, and they can compensate for each other (Richter and Hauff, 2022; Bokrantz and Dul, 2023; Dul, 2023). The shouldhave factors identified in the PLS-SEM analysis can only be unfolded if the must-have factors reach the required level (Richter et al., 2020). Instead of analysing the average relationships between dependent and independent variables, NCA identifies necessary conditions that cannot be compensated for in their absence (Richter et al., 2020; Dul, 2023). Therefore, the joint application of PLS and NCA is a profitable strategy to generate complementary findings in the research that allows advancing in theoretical development, it allows testing theoretical arguments referring to must-have factors and arguments referring to should-have criteria (Richter et al., 2020; Richter et al., 2022). When applying NCA together with PLS-SEM in the same work, it is 117 recommended to use the construct scores generated by PLS-SEM (Bolívar et al., 2022; Richter et al., 2022) since their generation considers the context of the structural model that represents the underlying theory and corresponds to the idea of verifying the needs in the context of PLS (Richter et al., 2020). Analytical induction is a method of qualitative analysis developed in sociology that seeks to identify conditions shared by a set of cases that present a specific outcome. Thus, explaining how a phenomenon happened is a valid methodology for assessing antecedent conditions linked to qualitative outcomes (Ragin, 2023). Analytical induction analysis aims to establish shared antecedent conditions, which could be interpreted as prescriptions describing and interpreting the phenomenon under investigation and combinations of causally relevant conditions (Acocella, 2023; Ragin, 2023). Analytical induction was introduced as a case-based generalisation strategy (Acocella, 2023; Wieringa & Danura, 2015). In addition, analytic induction combines theory testing with theory generation (Wilson et al., 2002). Subsequently, Ragin (2023) develops GAI, in which the existence of cases that do not present the conditions is made possible since its focus is on the antecedent conditions widely shared by cases that present a specific result (Ragin, 2023). It will be possible to visit the results of this research model, which was built and constituted by tested hypotheses, in the next chapter. 118 119 6. RESULTS 6.1 Introduction and objectives After presenting data from the selected sample, measurement scales and methods used, the results will be analysed through a quantitative approach. The first step in using PLS-SEM methodology is to assess the measurement model in this doctoral thesis. In this step, variables are tested for their reliability and validity using statistical indicators. Next, the structural model will be presented in accordance with the approach of Hair et al. (2021). The bootstrapping technique will be applied for a specified number of random samples. This will help to statistically test the relationships between the constructs hypothesized in Chapter 3. Followed, the research will showcase and verify the predictive power of the model. This will validate it empirically for their subsequent use and practical implications. Followed, the Necessary Condition Analysis (NCA), which is a method used to establish and identify the presence of necessary conditions, both in kind and degree, also will be presented its results. Then, the Generalized Analytic Induction (GAI) will be applied to analyse how the conditions that reflect social factors, and those that reflect strategic factors combine to explain the presence of a phenomenon. After, will be present the identification of necessary conditions using Quantitative Comparative Analysis (QCA), where will help us identify both types of conditions. And finaly, will be present the identification of suficcient conditions, also using Quantitative Comparative Analysis (QCA), also will help us identify both types of conditions. 6.2 PLS-SEM (Sufficient Causality) 6.2.1 Measurement Model The model's variables were estimated using Mode A. Since the constructions were created as artefacts, we relied on the correlation of the indicators. Henseler et al. (2016) claimed that reliability and validity measurements can be used in this case. The loadings of the indicators of the model were above 0.7 in terms of individual item dependability (as shown in Table 14). This suggests that the construct adequately accounts for over 50% of the indicator variance (Hair et al., 2021). The weights of the model indicators were also presented, revealing their importance. 120 We evaluated the internal consistency of the constructions using the composite reliability criteria. The constructs' composite dependability (CR) scores were greater than 0.7. Therefore, according to Riggs et al. (2023), the variables meet the requirement of construct reliability. We examined the average variance extracted (AVE) to evaluate convergent validity, and all construct values had AVEs greater than 0.5 (as shown in Table 14). Thus, the hidden variable accounts for more than 50% of the variance of the indicators (Hair et al., 2021). Construct/Indicator Loadings Weights RC AVE Leadership and top management commitment (LC) 0.927 0.618 LC1. The organisation's leaders and managers are committed to Quality 4.0. 0.777 0.148 LC2. Leaders and managers support efforts to improve Quality 4.0 initiatives. 0.753 0.139 LC3. Leaders and managers communicate the importance of Quality 4.0 to all levels of the organization. 0.820 0.161 LC4. Leaders and managers allocate adequate resources for Quality 4.0. 0.720 0.124 LC5. Leaders and managers encourage teamwork and problem-solving to achieve the specific objectives of Quality 4.0. 0.850 0.145 LC6. Leaders and managers motivate employees to implement Quality 4.0. 0.836 0.167 LC7. Leaders and managers devote efforts to training employees in Quality 4.0. 0.826 0.139 LC8. Leaders and managers evaluate Quality 4.0 initiatives based on objective facts and data. 0.736 0.122 LC9. Leaders and managers promote a culture of data-driven decisionmaking. 0.746 0.124 Quality 4.0 vision and strategy (VS) 0.892 0.233 0.933 0.780 VS1. The organisation is clear about what it wants to become by implementing Quality 4.0. 0.911 0.242 VS2. The Quality 4.0 strategy is aligned with the corporate strategy and objectives. 0.903 0.229 VS3. The Quality 4.0 strategy is communicated to all levels of the organisation. 0.892 0.231 VS4. The Quality 4.0 strategy is integrated with other operational methodologies (Lean, Six-sigma, excellence, quality management systems, etc.). 0.816 0.196 VS5. Employees understand the meaning of the Quality 4.0 strategy. 0.892 0.233 121 Human resource management for Quality 4.0 (HR) 0.942 0.716 HR1. Employees are aware of the benefits of implementing Quality 4.0. 0.853 0.213 HR2. Employees understand the changes that the implementation of Quality 4.0 will bring. 0.859 0.228 HR3. Workers have the emotional competencies (motivation, selfcontrol, empathy, relationship...) to handle the challenges of implementing Quality 4.0. 0.817 0.204 HR4. Employees drive improvement programmes related to Quality 4.0 0.797 0.231 HR5. The teams are qualified to solve problems related to Quality 4.0. 0.765 0.224 HR6. Employees receive continuous training on Quality 4.0 concepts and tools. 0.884 0.213 HR7. The organisation uses incentives, rewards and recognition for the implementation of Quality 4.0 projects and initiatives at a team level. 0.866 0.228 HR8. The organisation uses incentives, rewards and recognition for the implementation of Quality 4.0 projects and initiatives for individual employees. 0.901 0.204 HR9. The organisation maintains relationships and collaborates with entities or institutions that promote training in Quality 4.0. 0.863 0.231 Organisational culture towards Quality 4.0 (OC) 0.946 0.755 OC1. The organisational culture encourages employees to share information on Quality 4.0. 0.777 0.171 OC2. Organisational culture promotes data-driven management orientation at all levels. 0.753 0.167 OC3. Organisational culture promotes the shift to Quality 4.0 0.820 0.165 OC4. Organisations use specific strategies and programmes to raise employee awareness of the change involved in implementing Quality 4.0. 0.720 0.157 OC5. Employees are made to understand that the future of the organisation, and their own, will be better thanks to the implementation of Quality 4.0. 0.850 0.165 OC6. The organisation encourages and guides employees to act as change agents for Quality 4.0. 0.836 0.169 OC7. The organisation is looking for employees who can act as catalysts for change to motivate other employees to participate in implementing Quality 4.0. 0.826 0.157 Customer readiness (CR) 0.949 0.825 CR1. Implementing Quality 4.0 helps to know the characteristics of the product/service that customers value most. 0.899 0.213 128 Corner1 Condition SR Condition VS Corner4 Condition VS Figure 17. NCA ceiling lines. Source: Owner. To identify necessary conditions for RQ, we analysed the possibility that any of the conditions analysed had necessary causality in corners 1 and 4. We identified the effect sizes and calculated the p-values and c-accuracy rate for those cases with an appropriate effect size. Thus, SR is necessary in corner 1 (effectsize=0.371, p-value=0.000, c-accuracy=98.1%) and VS is necessary in corners 1 (effect-size=0.269, pvalue=0.000, c-accuracy=98.1%) and 4 (effect-size=0.243, p-value=0.000, c-accuracy=93.5%). The remaining relationships that might be necessary for effect size and p-value were discarded as they had a c-accuracy too far from the 95% threshold. Figure 17 shows the ceiling lines corresponding to the relationships that were necessary in kind. In all cases, the outcome under study is presented on the vertical axis (RQ) and the condition under analysis on the horizontal axis. In contrast, two conditions are necessary in corner 1 (SR and VS) and one in corner 4 (VS). While in the case of corner one, the necessary conditions can be interpreted as "a high value of SR or VS is necessary for RQ to have a high value", the relationship in corner 4 can be interpreted as "a low level of the VS condition is necessary for the RQ outcome to have a low value". NCA allows for the possibility that a factor simultaneously has two necessity relationships with the outcome, implying that more than one corner is empty, and the combined effect can be analysed. While the effect size is the sum of the individual effects, NCA calculates the p-value for the combined effect 129 (Dul, 2021). In the present case, the combined effect of VS in corners 1 and 4 were analysed (effectsize=0.512, p=0.000). Finally, to identify the necessary conditions in degree, NCA presents the bottleneck table (Dul et al., 2020). The bottleneck table is a tabular representation of the ceiling line and explains the condition levels to reach a particular outcome level (Dul, 2016). Although the conditions are presented together, this is a separate bivariate analysis of each condition (Table 19). Corner 1 Corner 4 RQ SR VS VS 0 NN NN NN 10 4.3 NN NN 20 12.5 NN NN 30 20.6 NN 94.6 40 28.8 7.5 86.3 50 37.0 19.8 78.0 60 45.2 32.2 69.7 70 53.4 44.5 61.5 80 61.6 56.8 53.2 90 69.8 69.1 44.9 100 77.9 81.5 36.6 RQ: Readiness for Quality 4.0; SR: Supplier readiness; VS: Quality 4.0 vision and strategy. NN: Not necessary Table 19. Bottleneck table (percentages). Source: Owner. Based on the results, we can establish that a high SR value is necessary for a high RQ value. Likewise, a high value of VS is necessary for a high value of RQ, and a low value of VS leads to a low value of RQ. 6.4 Identifying sufficient conditions with Generalised Analytic Induction (GAI) Next, GAI was applied to analyse how conditions reflecting social factors (LC, OC, and HR) and those reflecting strategic factors (VS, CR, SR) combined to explain the presence of a phenomenon. The objective of applying GAI is to identify the minimum set of conditions, widely shared across cases, that explain the outcome under study. Before its application, the 95th percentile, mean and 5th percentile were used to calibrate the data. First, the two models were run for the case of high Q4.0 readiness achievement (Table 20). 130 Raw coverage Unique coverage Social factors OC*HR 0.754 0.754 Solution coverage 0.754 - Strategics factors CR*SR 0.796 0.796 Solution coverage 0.796 - Note: OC: Organizational culture towards Quality 4.0; HR: Human resource management for Quality 4.0; CR: Customer readiness; SR: Supplier readiness. *= Boolean and (the conjunction of the terms) Table 20. Models for high Quality 4.0 readiness. Source: Owner. As seen in Table 20, when we analyse the social factors, the OC*HR configuration is sufficient to explain 75.4% of the cases with a high Q4.0 readiness. On the other hand, when strategic factors are combined, the minimum combination of conditions is CR*SR, explaining 79.6%. Next, the combinations of conditions that explain not achieving a high level of Q4.0 readiness were analysed (Table 21). Raw coverage Unique coverage Social factors ~OC*~HR 0.617 0.064 ~LC*~OC 0.574 0.024 LC*OC*HR 0.268 0.060 Solution coverage 0.715 Strategics factors ~VS*~CR 0.855 0.855 Solution coverage 0.855 - Note: OC: Organizational culture towards Quality 4.0; HR: Human resource management for Quality 4.0; LC: Leadership and top management commitment; VS: Quality 4.0 vision and strategy; CR: Customer readiness. *= Boolean and (the conjunction of the terms); ~ = negation of the condition (not achieving a high level) Table 21. Models with denial of a high degree of Quality 4.0 readiness. Source: Owner. First, when social factors are taken as explanatory conditions, three possible combinations of conditions are presented: ~OC*~HR (raw coverage=0.617), ~LC*~OC (raw coverage=0.574) and LC*OC*HR (raw coverage=0.268). The first of these implies the negation of the combination of conditions that explained a high level of Q4.0 readiness (~OC*~HR), although two other conjunctions emerge. In the second, the combination of the negation of two conditions under analysis (~LC*~OC) is presented. The third of the conjunctions is more challenging for the research since it presents us with the case of some 131 organisations (coverage is only 0.268), which, while simultaneously presenting the three social factors considered, are not sufficient for the organisation to achieve a high level of Quality readiness. Finally, when the strategic factors are combined to explain the denial of achieving a high level of Q4.0 readiness, the minimum combination of conditions is ~VS*~CR (raw coverage=0.855). 6.5 Identifying the necessary conditions with Qualitative Comparative Analysis (QCA) QCA is an asymmetric analysis technique that combines the logic and empirical intensity of qualitative approaches, which are rich in contextual information, with quantitative methods that handle large numbers of cases and are more generalisable (Pappas & Woodside, 2021). The output of QCA is more detailed and explores multiple causes, synthesising the richness of case data into more concise descriptions (Vis, 2012). Since its inception, QCA has been used for both theory development and testing, as well as data analysis (Geremew et al., 2023). QCA aims to bridge the divide between the quantitative and qualitative paradigms: it combines inductive and comparative case-based methods and techniques with quantitative approaches (which generate more generalisable results than symmetrical analyses). In this way, QCA combines the methodological discipline and rigour of quantitative research with the causal complexity and inductive sensitivity of qualitative analysis (Geremew et al., 2023). QCA is characterised by answering research questions linked to causes that generate effects, by a formalised case study, a set-theoretic approach and by responding to causal complexity (Oana et al., 2021). Within the latter, QCA applies three epistemological assumptions: asymmetry, equifinality and joint causation. According to asymmetry an outcome and its negation require different explanations. In other words, the explanations of a phenomenon and its negation differ and are not simply the reversal of the behaviour of the causes. Equifinality implies that an outcome can be explained by different combinations of causes. Joint causation implies that it is the combination of conditions that explains the generation of a given outcome. Also, according to equifinality, the impact of a condition may depend on the conditions with which it is combined. But possibly one of the greatest riches of the analysis supported by QCA is that it identifies necessary and sufficient conditions. Necessary conditions are those without which the outcome under study cannot occur, i.e. their absence anticipates that the outcome will not occur. Sufficient conditions, on the other hand, are those that explain the presence of the outcome, but the presence of the outcome does not in any case require them. The first step in a QCA analysis is the calibration of the data, applying the 95th percentile (total inclusion), the mean (for the cross-over point) and the 5th percentile (total exclusion), as presented on the followed table 22. 132 Cons.Nec Cov.Nec RoN RC 0.871/0.356 0.855/0.321 0.859/0.566 HR 0.969/0.582 0.804/0.444 0.751/0.516 LC 0.903/0.629 0.801/0.514 0.780/0.592 OC 0.941/0.551 0.851/0.459 0.832/0.577 SR 0.953/0.605 0.792/0.462 0.741/0.525 VS 0.842/0.361 0.873/0.345 0.887/0.603 ~RC 0.309/0.839 0.342/0.857 0.632/0.887 ~HR 0.330/0.743 0.461/0.956 0.758/0.975 ~LC 0.453/0.757 0.570/0.877 0.767/0.920 ~OC 0.402/0.821 0.493/0.928 0.728/0.950 ~SR 0.353/0.728 0.492/0.935 0.768/0.963 ~VS 0.369/0.867 0.386/0.835 0.621/0.859 Table 22. Analysis of necessary conditions RQ/ ~RQ. Source: Owner. As can be seen in the table above, four conditions are necessary for the presence of RQ as they exceed the required thresholds of consistency (0.9) and coverage (0.5). Also, an RoN close to 0.5 would be a cause for concern. The four conditions that exceed the criteria for RQ are HR (Cons.Nec=0.969, Cov.Nec=0.804, Ron=0.751), LC (Cons.Nec=0.903, Cov.Nec=0.801, Ron=0.780), OC (Cons.Nec=0.941, Cov.Nec=0.851, Ron=0.832) y SR (Cons.Nec=0.953, Cov.Nec=0.792, Ron=0.741). Therefore, we can state that for a company to be ready to implement Quality 4.0, HR, LC, OC and SR must be present. On the contrary, the presence, or negation, of any of the conditions is not necessary for an organisation not to have a high degree of readiness for Quality 4.0 (~RQ). 6.6 Identifying the sufficient conditions with Qualitative Comparative Analysis (QCA) The parsimonious and intermediate solution were then calculated. While the parsimonious solution is the one that incorporates in the minimisation process all those logical remnants that allow a more synthetic solution to be obtained, the intermediate solution only incorporates those that meet the directional expectations. RQ's parsimonious solution resulted in the following results: CR + VS + ~HR*OC + ~HR*SR + HR*~LC + HR*~OC -> RED, presenting on the following table 23. 133 inclS PRI covS covU Cases CR 0.855 0.787 0.871 0.019 66; 48, 59; 52, 58, 60, 61, 63, 89, 91; 54; 56; 49 ,85; 50 ,51; 53 , 55, 57, 62, 64, 65, 67, 68, 69, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 87, 88, 90, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106 VS 0.873 0.813 0.842 0.015 15; 66; 52,58, 60, 61, 63, 89, 91; 56; 49, 85; 53, 55, 57, 62, 64, 65, 67, 68, 69, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 87, 88, 90, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106 ~HR*OC 0.904 0.352 0.315 0.000 37,38; 26 ~HR*SR 0.850 0.244 0.318 0.000 20, 21, 25, 28; 26 HR*~LC 0.906 0.650 0.438 0.000 32, 39; 33, 40, 41; 45; 66; 48, 59; 52, 58, 60, 61, 63, 89, 91 HR*~OC 0.868 0.421 0.389 0.000 32, 39; 33, 40, 41; 35, 36; 29; 54; 56 Model 0.775 0.661 0.993 - inclS: inclusion for sufficiency; PRI: proportional reduction of inconsistency; covS: coverage of the solution; covU: unique coverage Table 23. RQ parsimonious solution. Source: Owner. The solution is composed of six terms and has high parameters (inclS=0.775, PRI=0.661, covS=0.993). Recall that inclS is sometimes assimilated with the significance level and covS with the R2. The first two terms are composed of isolated conditions, CR (inclS=0.855, PRI=0.787, covS=0.871) and VS (inclS=0.873, PRI=0.813, covS=0.842). In the next four terms, HR is related to different conditions, always showing the negation of one of the conditions ~HR*OC (inclS=0.904, PRI=0.352, covS=0.315), ~HR*SR (inclS=0.850, PRI=0.244, covS=0.318), HR*~LC (inclS=0.906, PRI=0.650, covS=0.438) and HR*~OC (inclS=0.868, PRI=0.421, covS=0.389). Among these last four terms, three of them have a low PRI, so these terms should be taken with caution. It should also be noted that there are no terms with a high unique coverage. Furthermore, the parsimonious solution of ~RQ was identified: ~OC + ~SR + ~VS -> ~RED, presenting on the following table 24. 134 inclS PRI covS covU Cases ~OC 0.928 0.871 0.821 0.021 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 22; 15; 24; 20, 21, 25, 28; 32, 39; 33, 40, 41; 35, 36; 29; 54; 56 ~SR 0.935 0.883 0.728 0.015 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 22; 15; 24; 37,38; 32, 39; 35, 36; 66; 49, 85 ~VS 0.835 0.745 0.867 0.098 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 22; 24; 20, 21, 25, 28; 37, 38; 26; 32, 39; 33, 40, 41; 45; 35, 36; 29; 27, 30, 31, 34, 42, 43, 44, 46, 47; 48, 59; 54; 50, 51 Model 0.801 0.687 0.963 - inclS: inclusion for sufficiency; PRI: proportional reduction of inconsistency; covS: coverage of the solution; covU: unique coverage Table 24. ~RQ parsimonious solution. Source: Owner. The solution is composed of three terms, again showing high parameters (inclS=0.801, PRI=0.687, covS=0.963). In this case, the solution is composed of three independent terms ~OC (inclS=0.928, PRI=0.871, covS=0.821), ~SR (inclS=0.935, PRI=0.883, covS=0.728) and ~VS (inclS=0.835, PRI=0.645, covS=0.867). Outcome RQ Outcome ~ RQ Figure 18. Graphical representation of parsimonious solutions. Source: Owner. The intermediate solutions were then calculated. The intermediate solution of RQ resulted in ~CR*VS + ~CR*HR*~OC + HR*OC*VS + ~CR*~HR*LC*OC + CR*HR*LC*SR + HR*~LC*OC*SR + ~CR*~HR*LC*SR -> RQ, as presented on the followed table 25. 135 inclS PRI covS covU Cases ~CR*VS 0.895 0.470 0.196 0.003 15 ~CR*HR*~OC 0.820 0.080 0.269 0.008 32, 39; 33, 40, 41; 35, 36; 29 HR*OC*VS 0.961 0.939 0.796 0.051 66; 52, 58, 60, 61, 63, 89, 91; 49, 85; 53, 55, 57, 62, 64, 65, 67, 68, 69, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 87, 88, 90, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106 ~CR*~HR*LC*OC 0.888 0.152 0.251 0.001 37, 38; 26 CR*HR*LC*SR 0.972 0.953 0.768 0.029 54; 56; 50,51; 53, 55, 57, 62, 64, 65, 67, 68, 69, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 87, 88, 90, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106 HR*~LC*OC*SR 0.956 0.800 0.424 0.005 45; 48, 59; 52, 58, 60, 61, 63, 89, 91 ~CR*~HR*LC*SR 0.839 0.104 0.253 0.000 20, 21, 25, 28; 26 Model 0.855 0.765 0.955 - inclS: inclusion for sufficiency; PRI: proportional reduction of inconsistency; covS: coverage of the solution; covU: unique coverage Table 25. Intermediate solution RQ. Source: Owner. A solution is left consisting of seven terms and showing optimal parameters (inclS=0.855, PRI=0.765, covS=0.955). The terms are ~CR*VS (inclS=0.895, PRI=0.470, covS=0.196), ~CR*HR*~OC (inclS=0.820, PRI=0.080, covS=0.269), HR*OC*VS (inclS=0.961, PRI=0.939, covS=0.796), ~CR*~HR*LC*OC (inclS=0. 888, PRI=0.152, covS=0.251), CR*HR*LC*SR (inclS=0.972, PRI=0.953, covS=0.768), HR*~LC*OC*SR (inclS=0.856, PRI=0.800, covS=0.424) and ~CR*~HR*LC*SR (inclS=0.839, PRI=0.104, covS=0.253). While most of the parameters are remarkably high, terms 2, 4 and 6 have very low PRI values. Finally, the intermediate solution of ~RQ was obtained: ~VS + CR*~OC + CR*~SR + HR*~LC*~OC*~SR -> ~RQ, as presented on the followed table 26. inclS PRI covS covU Cases ~VS 0.835 0.745 0.867 0.260 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 22; 24; 20, 21, 25, 28; 37, 38; 26; 32, 39; 33, 40, 41; 45; 35, 36; 29; 27, 30, 31, 34, 42, 43, 44, 46, 47; 48, 59; 54; 50,51 CR*~OC 0.856 0.033 0.277 0.021 54; 56 CR*~SR 0.880 0.044 0.252 0.015 66; 49, 85 ~HR*~LC*~OC*~SR 0.991 0.984 0.578 0.013 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 22; 15 Model 0.807 0.694 0.954 - inclS: inclusion for sufficiency; PRI: proportional reduction of inconsistency; covS: coverage of the solution; covU: unique coverage Table 26. Intermediate solution ~RQ. Source: Owner. This solution is composed of four terms and has optimal parameters (inclS=0.807, PRI=0.694, covS=0.954). The terms are ~VS (inclS=0.835, PRI=0.745, covS=0.867), CR*~OC (inclS=0.856, 136 PRI=0.033, covS=0.277), CR*~SR (inclS=0.880, PRI=0.044, covS=0.252) and ~HR*~LC*~OC*~SR (inclS=0.991, PRI=0.984, covS=0.578). The situation described above is repeated, where remarkable parameters are constrained by the value of PRI in terms 2 and 3, and presented on the followed figure 19. Outcome R Q Outcome ~R Q Figure 19. Graphical representation of intermediate solutions. Source: Owner. Finally, the joint presentation allows to differentiate the core conditions (they are part of the parsimonious solution) from the peripheral conditions (they complete the intermediate solution), and presented on the followed table 27. Term1 Term2 Term3 Term4 Term5 Term6 Term7 CR HR LC SR VS OC inclS 0.972 0.895 0.888 0.839 0.956 0.820 0.961 PRI 0.953 0.470 0.152 0.104 0.800 0.080 0.939 covS 0.768 0.196 0.251 0.253 0.424 0.269 0.796 covU 0.029 0.003 0.001 0.000 0.005 0.008 0.051 Large/small circles: part of the parsimonious/intermediate solution Filled/empty circle: present/denied condition Table 27. Identification of core and peripheral conditions of RQ. Source: Owner. Again, the intermediate solution is composed of terms with high values of inclS and covS, although terms 2, 3, 4 and 6 have a low value of PRI. The first and seventh terms are the only ones that are composed of the presence of conditions, although in term 7 these are peripheral conditions. That is, they are not part of the parsimonious solution. Also, both terms only share the presence of HR, in the case of 137 the first term it is combined with CR*LC*SR, while in the seventh term with VS and OC. Terms 2, 3, 4 and 5 have ~CR. Terms 3 and 4 are very similar since in term 3 it is combined with ~HR*LC*OC and in term 4 with ~HR*LC*SR. Likewise, while in term 2 ~CR*VS is associated with ~CR*HR*~OC in term 6. Finally, term 5 combines the presence and negation of core and peripheral conditions (HR*~LC*SR*OC). In these solutions, both equifinality (there are 7 terms that explain the presence of RQ) and joint causation according to which the effect of a condition may depend on those with which it is combined. For example, while in terms 3 and 4 HR is negated, in terms 4 and 5 it is present. It seems that the effect of HR is strongly related to LC. Likewise, the same exposition is made for the explanatory solutions of ~RQ, as it observed on the followed table 28. Term1 Term2 Term3 Term4 CR HR LC SR VS OC inclS 0.835 0.856 0.880 0.991 PRI 0.745 0.033 0.044 0.984 covS 0.867 0.277 0.252 0.578 covU 0.260 0.021 0.015 0.013 Large/small circles: part of the parsimonious/intermediate solution Filled/empty circle: present/denied condition Table 28. Identification of core and peripheral conditions of ~RQ. Source: Owner. In the intermediate solution of ~RQ, different negated core conditions appear. Thus in term 1 VS, in term 2 OC and in term 3 SR. Except for the first term, which consists exclusively of ~VS, the rest of the terms combine the presence of CR or HR with the negation of other conditions. Thus, term 2 results in CR*~OC, term 3 in CR*~SR and term 4 in HR*~LC*~SR*~OC. In this way, we can see how some of the attributes studied (CR and HR) explain why an organisation is not ready to implement quality 4.0 when combined with the negation of other conditions. Among the terms that explain the organisation's readiness to implement quality 4.0, there are terms that only consider strategic factors (~CR*VS), others with a balance between social and strategic factors 144 7.2 Theoretical and Management Implications Finally, and at the end of this chapter, it is important to understand what this study in the form of a systematic literature review (RSL) contributed or added, both in theoretical terms, that is, what it managed to contribute to the body of knowledge on Quality 4.0, as well as in practical and management terms, also realizing the usefulness of its results for managers, thus justifying its contribution and usefulness, it is also important to mention that according to Hair et al. (2020), the use of the PLS-SEM methodology was highly attractive for providing explanatory and predictive insights, as it was particularly effective in providing causal explanations for the development of managerial implications. Among the theoretical contributions of this study, the suitability of analysing from the perspective of embedded necessity theories to explain the level of preparedness of organisations to implement Q4.0 stands out, this is due to the existence of sufficient and necessary conditions. As it saw before, this suitability is even more remarkable because one of the necessary conditions is not signicant (Supplier readiness). In other words, using only a conventional analysis method would have resulted in establishing an irrelevant role for Supplier readiness, whereas for Readiness for Q4.0 to reach high values, Supplier readiness will also have to do so. In addition, it analyses the combination of social and strategic factors in Q4.0 that drive high and low levels of preparedness. At this stage of our work we must be aware of the factors that have led us to this point, where it is extremely important to keep in mind some of the basic concepts under study. Therefore, and as a theoretical contribution, it is worth remembering what necessary conditions and sufficient conditions are, as well as the principle of causality that is implicit in these same concepts. It is worth remembering that all of them were developed throughout this work, with particular focus on the methodology chapter where it was possible to focus. In this way, and as a practical contribution to management and managers, it is possible to identify that according to Dul (2023), the success of complex organizational changes depends on several factors, but only some are essential, which according to Sony and Naik (2020), is central Identifying the crucial factors for the successful implementation of new processes and projects in organizations, which for Richter et al. (2020), Richter and Hauff (2022), understanding the essential factors for the implementation of management practices is also important , which for Sony et al. (2021) and Antony et al. (2023a), organizations must assess their preparedness for Q4. 0 before implementing it, and that for Richter et al. (2020), Bokrantz and Dul (2023), ignoring the necessary conditions in research can lead to incomplete conclusions and recommendations when implementing them. The results achieved with this research will allow progress in the future in the field of Q4.0, providing a better understanding of Q4.0 readiness factors and how they can be used to analyze an organization's readiness to implement Q4.0. Furthermore, it can help managers differentiate between necessary, 145 sufficient and necessary and sufficient readiness factors, thus guiding the successful implementation of Q4.0 and optimizing decision-making in this area, thus becoming another contribution for Management. In terms of the contribution that this study can bring to the field of Quality 4.0 knowledge (theoretical implication), it is known that academic research currently faces a critical problem: there is a mismatch between methodology and theoretical assumptions about cause and effect (Linder et al., 2023). Therefore, according to Bokrantz and Dul (2023), and Linder et al. (2023), to achieve a theory-method fit, it is essential to use an appropriate methodology. Theories that encompass necessary and sufficient relations are called embodied necessity theories. These theories improve on traditional approaches by assuming that although all factors may contribute to producing the outcome, only some may be necessary (Richter et al., 2020; Dul, 2021; Bokrantz and Dul, 2023). An empirical study that aims to develop and test embodied need theories should use a traditional analysis method, such as structural equation modeling (SEM), along with a method that can analyze need to ensure a good fit between theory and method (Bokrantz and Dul, 2023). This is justified because conventional quantitative methods such as SEM do not encompass the logic of need and, therefore, cannot analyze the necessary conditions (Frommeyer et al., 2022; Bokrantz and Dul, 2023; Dul, 2023). The use of necessary conditions analysis (NCA) can help resolve this issue (Richter and Hauff, 2022; Linder et al., 2023), as this ensures a fit between theory and method when the theory involves necessary relationships between factors and results that require empirical evaluation (Dul, 2021; Dul, 2023), which is considered another great theoretical contribution that was brought by this work. Concerning the practical or management implications, firstly, it is necessary to include Q4.0 among the strategic objectives of the organisation to consider that the organisation is prepared to implement Q4.0 successfully. Thus, considering Q4.0 as a strategic issue makes it possible to implement it, and the more precise the organisation is about how vital Q4.0 is for achieving competitive advantages and its future, the more prepared the organisation will be to implement it. Secondly, the involvement of suppliers, as a critical link in the organisation's supply chain, is a prerequisite for the organisation to embark on any Q4.0 project. Thirdly, once the organisation is clear about the strategic importance of Q4.0 and has the involvement of suppliers, to increase their level of preparation for the implementation of Q4.0 and have some guarantee of success, it must commit leaders and top management; form an organisational culture that encourages risk-taking, challenges and creativity; involve and train workers, as well as reward their efforts to implement Q4.0; and be permanently focused on customers, i.e., the implementation of Q4.0 makes sense as long as it contributes to satisfying the needs and expectations of customers. Still from a practical or managerial point of view, identifying combinations of social and strategic factors that lead to high and low levels of Q 4.0 readiness confirms the need to implement Q 4.0 from a systemic point of view, understanding that there are relationships and synergies between the readiness factors. 146 In order to systematize the theoretical and practical implications of what was previously described, it is worth paying attention to the following, as theoretical implications: (1) attempt to explain, in the light of needs theory, the level of preparation of organizations for implement Q4.0, (2) analysis of the combination of social and strategic factors in Q4.0 that drive different levels of organizational preparation, (3) Provide knowledge of the concepts of necessary conditions and sufficient conditions, as well as the principle of causality in them. implicit, (4) In this empirical study, the attempt to use the appropriate methodology in order to adjust the method with the theory, when the theory involves necessary relationships between factors, in order to produce the adjusted and expected result, when this requires empirical evaluation. And as practical implications: (1) Identification of crucial factors for the successful implementation of new Q4.0 Systems processes and projects in organizations, (2) Identify and understand the essential factors for the implementation of management practices within the scope of implementation of a Q4.0 system, (3) Identify the success factors for organizational changes, within the scope of implementing a Q4.0 system, (4) Assess your degree of preparation or readiness to receive a 4.0 quality management system, (5) Help managers differentiate between necessary, sufficient and necessary and sufficient readiness factors, thus guiding the success of Q4.0 implementation, (6) Must include Q4.0 among the organization's strategic objectives to consider that the. organization is prepared to implement Q4.0 system successfully, (3) Involve suppliers as a critical link in the organization's supply chain, considering it as a prerequisite for the organization to embark on any Q4.0 project. leadership and senior management in the process of implementing a Q4.0 system, also being a prerequisite, (5) involving suppliers, as a critical link in the organization's supply chain, also being a prerequisite for the organization to embark on any project Q4.0, (6) engage and train employees, and reward them for their efforts in implementing Q4.0, and (7) Customer focus. It is also worth highlighting that this research work becomes a fundamental contribution to Organizations in general, as well as to knowledge management, as it can become a guide to be followed by them in their intention to implement a system of quality management in the context of the 4th industrial revolution. It also becomes an opportunity for Organizations to become aware of the factors that can influence and that are the basis for preparation for its implementation, and may or may not be sufficient and/or necessary conditions for this. As for Knowledge Management in general, this work also constitutes a fundamental contribution, as it brings to academia the approach to concepts and techniques in the field of Management, such as Quality 4.0, Industry 4.0, PLS-SEM, Analysis of Necessary Conditions, Generalized Analytical Induction and Qualitative Comparative Analysis (QCA). 147 8. CONCLUSIONS, LIMITATIONS AND FUTURE LINES REASERCH This research has been developed also to fill the gap identified by the lack of studies that explain the explanatory factors of the level of readiness of organisations to implement Q4.0, both from their shouldhave and must-have roles. This is because most research has focused on the factors that produce specific results (should have), although those factors that facilitate them (must have) are equally crucial for decision-making (Van der Valk et al., 2016). It identified the social and strategic readiness factors that an organisation should consider before implementing Q4.0-related projects (Antony et al., 2023b). In response to the first objective, it is found that Q4.0 vision and strategy, Customer readiness, Human resource management for Q4.0, Leadership and top management commitment and Organisational culture towards Q4.0 are sufficient factors for organisations to be ready to implement Q4.0. About the second research objective, it is confirmed that Q4.0 vision and strategy and Supplier readiness are necessary conditions for Readiness for Q4.0. Thus, unless Q4.0 vision and strategy and Supplier readiness reach a certain level, Q4.0 readiness cannot occur. Finally, combinations of social and strategic factors of Q4.0 readiness have been identified as leading to high and low readiness levels. When interpreting the results and drawing conclusions, several limitations must be considered. Firstly, the sample comprises Spanish organisations with EFQM excellence recognition systems. In this sense, factors such as the country's culture, economy, technological development, or educational level could limit the extrapolation and generalisation of the study's results. This factor raises the possibility of expanding the sample for comparative or more global studies. Secondly, the influence of the business environment on readiness for Q4.0 could be analysed, for example, the effects of regulatory changes, industry standards, competitive pressure, or technological trends. Thirdly, further studies could consider including aspects related to technological and digitisation infrastructure (Antony et al., 2022) and data analytics capabilities (Maganga and Taifa, 2023) in analysing readiness factors. Fourthly, PLS-SEM assumes linear relationships between variables. Although, in the proposed model, direct linear relationships were established between the readiness factors and the readiness of the organisation to implement Q4.0, there could also be inverse relationships in this context and even relationships between the identified readiness factors. These relationships should be considered in future research. Moreover, the necessary conditions have been analysed independently, and their joint effect cannot be identified. In addition, future research can test the possible moderating effects of contextual factors, such as the level of excellence achieved by the organisation, its size, or the sector of activity. Furthermore, it could be possible to analyse whether different combinations of conditions explain the presence of the outcome under study through configurational techniques. 148 149 9. BIBLIOGRAPHIC REFERENCES Abdullah, F. M., Saleh, M., Al-Ahmari, A. M., and Anwar, S. (2022). The impact of industry 4.0 technologies on manufacturing strategies: proposition of technology-integrated selection. IEEE Access, 10, 21574-21583. Agwu, O. E., Akpabio, J. U., Alabi, S. B., and Dosunmu, A. (2018). Artificial intelligence techniques and their applications in drilling fluid engineering: a review. Journal of Petroleum Science and Engineering, Elsevier B.V., 167, 300-315. Albers, A., Gladysz, B., Pinner, T., Butenko, V., and Stürmlinger, T. (2016). Procedure for defining the system of objectives in the initial phase of an industry 4.0 project focusing on intelligent quality control systems. Procedia Cirp, 52(1), 262–267. Aldag, M. C., and Eker, B. (2018). What is Quality 4.0 in the era of industry 4.0. In Proceedings of the 3rd International Conference on Quality of Life, (pp. 28-30). Kopaonik, Serbia. Ali, K., and Johl, S. K. (2023). Impact of total quality management on industry 4.0 readiness and practices: does firm size matter? International Journal of Computer Integrated Manufacturing, 36(4), 567-589. Ali, K., and Johl, S. K. (2021). Soft and hard TQM practices future research agenda for industry 4.0. Total Quality Management and Business Excellence, 33 (13-14), 1625-1655. Allen, R. S., and Kilmann, R. H. (2001). The role of the reward system for a Total Quality Managementbased strategy. Journal of Organizational Change Management, 14(2), 110-131. Allmendinger, G. and Lombreglia, R. (2005). Four strategies for the age of smart services. Harvard Business Review, 83(10), 131. Antony, J., Kaul, A., Bhat, S., Sony, M., Kaul, V., Zulfiqar, M., and McDermott, O. (2024). Critical failure factors for Quality 4.0: an exploratory qualitative study. International Journal of Quality and Reliability Management, 41(4), 1044-1062. Antony, J., McDermot, O. and Sony, M. (2022). Quality 4.0 conceptualisation and theoretical understanding: a global exploratory qualitative study. The TQM Journal 34 (5), 1169-1188. Antony, J., McDermott, O., Sony, M., Toner, A., Bhat, S., Cudney, E. A., and Doulatabadi, M. (2023). Benefits, challenges, critical success factors and motivations of Quality 4.0 – A qualitative global study. Total Quality Management and Business Excellence, 34(7-8), 827-846. Antony, J., Sony, M. and Cudney, E. (2020). Quality 4.0: motivations and challenges from a pilot survey in European. Future Factory, 1-7. 150 Antony, J., Sony, M., Furterer, S., McDermott, O., and Pepper, M. (2022). Quality 4.0 and its impact on organizational performance: an integrative viewpoint. The TQM Journal, 34(6), 2069-2084. Antony, J., Sony, M., McDermott, O. (2023). Conceptualizing Industry 4.0 readiness model dimensions: an exploratory sequential mixed-method study. The TQM Journal, 35(2), 577 – 596. Antony, J., Sony, M., McDermott, O., Jayaraman, R., and Flynn, D. (2023). An exploration of organizational readiness factors for Quality 4.0: an intercontinental study and future research directions. International Journal of Quality and Reliability Management, 40 (2), 582 - 606. Antony, J., Swarnakar, V., Sony, M., McDermott, O., and Jayaraman, R. (2023). How do organizational performances vary between early adopters and late adopters of Quality 4.0? An exploratory qualitative study. The TQM Journal. Arsovski, S. (2019). Social Oriented Quality: From Quality 4.0 Towards Quality 5.0. Quality Festival 2019, (Vol. 13, pp. 397-404). Asif, M. (2020). Are QM models aligned with Industry 4.0? A perspective on current practices. Journal of Cleaner Production, 258. Augot, D., Chabanne, H., Clemot, O. and George, W. (2018). Transforming face-to-face identity proofing into anonymous digital identity using the bitcoin blockchain. Proceedings - 2017 15th Annual Conference on Privacy, Security and Trust (PST), (pp. 25-34). Avis, J. (2018). Socio-technical imaginary of the fourth industrial revolution and its implications for vocational education and training: a literature review. Journal of Vocational Education and Training, 70 (3), 337-363. Azuma, R.T. (1997). A survey of augmented reality. Presence: Teleoperators and Virtual Environments, 6(4), 355-385. Babatunde, O. K. (2021). Mapping the implications and competencies for Industry 4.0 to hard and soft total quality management. The TQM Journal, 33(4). Bag, S., Yadav, G., Dhamija, P., Kataria, K.K. (2021). Key resources for industry 4.0 adoption and its effect on sustainable production and circular economy: An empirical study. Journal of Cleaner Production, 281, 125233. Behera, R. K., Bala, P., K., and Dhir, A. (2019). The emerging role of cognitive computing in healthcare: a systematic literature review. International journal of medical informatics, 129, 154-166. Benitez-Amado, J., Henseler, J., and Castillo, A. (2017). Development and Update of Guidelines to Perform and Report Partial Least Squares Path Modelling in Information Systems Research. PACIS 2017 Proceedings, 86. 151 Bertsimas, D., and Kallus, N. (2020). From predictive to prescriptive analytics. Management Science, 66(3), 1025-1044. Bibby, L. and Dehe, B. (2018). Defining and assessing industry 4.0 maturity levels–case of the defence sector. Production Planning and Control by Taylor and Francis, 29 (12), 1030-1043. Billinghurst, M., Clark, A. and Lee, G. (2014). A survey of augmented reality. Foundations and Trends in Human-Computer Interaction, 8(2-3), 73-272. Bilstein, N. and Stummer, C. (2020). Special section: multidisciplinary perspectives on challenges in managing smart products and services. Schmalenbach Business Review, 72(1), 479-483. Black, S. A., and Porter, L. J. (1996). Identification of the critical factors of TQM. Decision sciences, 27(1), 1-21. Bocoya-Maline, J., Rey-Moreno, M., and Calvo-Mora, A. (2024). The EFQM excellence model, the knowledge management process and the corresponding results: an explanatory and predictive study. Review of Managerial Science, 18(15), 1281 - 1315. Bodrow, W. (2017). Impact of Industry 4.0 in service oriented firm, Adv. Manuf., 5, 394–400. Bokrantz, J. and Dul, J. (2023). Building and testing necessity theories in supply chain management. Journal of Supply Chain Management, 59, 48-65. Bolívar, L. M., Roldán, J. L., Castro-Abancéns, I., and Casanueva, C. (2022). Velocidade de expansão internacional: O papel mediador da mobilização de recursos de rede. Management International Review, 62(4), 541-568. Bresciani, S., Ferraris, A., Romano, M., and Santoro, G. (2021). Digital Transformation Management for Agile Organizations: A compass to sail the digital world. Emerald Group Publishing. Brettel, M., Friederichsen, N., Keller, M., and Rosenberg, M. (2014). How virtualization, decentralization and network building change the manufacturing landscape: An Industry 4.0 Perspective. International Journal of Mechanical, Industrial Science and Engineering, 8(1), 37–44. Bruccoleri, M., Pasek, Z. J., and Koren, Y. (2006). Operation management in reconfigurable manufacturing systems : reconfiguration for error handling. International Journal of Production Economics, 100(1), 87-100. Bryde, D., Leighton, D. (2009). Improving HEI Productivity and Performance through Project Management, Educational Management Administration and Leadership, 37(5), 705–721. Buyukozkan, G., and Goçer, F. (2018). Digital supply chain: literature review and a proposed framework for future research, Computers in Industry, 97, 157-177. 152 Calabrese, A., Levialdi Ghiron, N., and Tiburzi, L. (2021). Evolutions and revolutions in manufacturers implementation of industry 4.0: a literature review, a multiple case study, and a conceptual framework. Production Planning and Control, 32(3), 213-227. Calvo-Mora, A., Blanco-Oliver, A., Roldán, J. L., and Periáñez-Cristóbal, R. (2020). TQM factors and organisational results in the EFQM excellence model framework: an explanatory and predictive analysis. Industrial Management and Data Systems, 120 (12), 2297-2317. Calvo-Mora, A., Navarro-García, A., Periañez-Cristobal, R. (2015). Project to improve knowledge management and key business results through the EFQM excellence model, International Journal Projedt Management, 33(8), 1638–1651. Calvo-Mora, A., Picón, A., Ruiz, C., and Cauzo, L. (2014). The relationships between soft-hard TQM factors and key business results. International Journal of Operations and Production Management, 34(1), 115-143. Cameron, K. and Quinn, R. (1999). Diagnosing and Changing Organizational Culture. In Addison- Wesley, (Ed.). Series on Organization. Campatelli, G., Citti, P., Meneghin, A. (2011). Development of a simplified approach based on the EFQM model and Six Sigma for the implementation of TQM principles in a university administration. Total Quality Management and Business Excellence, 22(7), 691–704. Canbay, K., and Akman, G. (2023). Investigating changes of total quality management principles in the context of Industry 4.0: Viewpoint from an emerging economy. Technological Forecasting and Social Change, 189, 122358. Carmona-Calvo, M. A., Suárez, E. M., Calvo-Mora, A. and Periáñez- Cristóbal, R. (2016). Quality management systems: a study in firms in southern Spain and northern Morocco. European Research on Management and Business Economics, 22 (1), 8-16. Carvalho, A. M., Dias, A. R., Dias, A. M. and Sampaio, P. (2024). The Quality 4.0 Roadmap: Designing a capability roadmap toward quality management in Industry 4.0. Quality Management Journal, 31(2), 117-137. Carvalho, A. M., Sampaio, P., Rebentisch, E., and Saraiva, P. (2021a). 35 years of excellence, and perspectives ahead for excellence 4.0. Total Quality Management and Business Excellence, 32(11-12), 1215-1248. Carvalho, A. V., Enrique, D. V., Chouchene, A. and Charrua-Santos, F. (2021b). Quality 4.0: an overview, Procedia Computer Science, 181, 341-346. 153 Chan, K. Y., Dillon, T. S., and Kwong, C. K. (2011). Modeling of a liquid epoxy molding process using a particle swarm optimization-based fuzzy regression approach. IEEE Transactions on Industrial Informatics, 7(1), 148-158. Chatha, K.A. and Butt, I. (2015). Themes of study in manufacturing strategy literature. International Journal of Operations and Production Management, 35(4), 604-698. Chatha, K. A., Butt, I., Jajja, M. S. S., and Arshad, M. (2018). Theoretical developments in empirical quantitative manufacturing strategy literature. International Journal of Operations and Production Management, 38(1), 183-210. Chen, Z., and Huang, L. (2020). Digital twins for information-sharing in remanufacturing supply chain: a review. Energy, 220(1), 119712-119741. Cheng, G.,Liu, L., Qiang, X., and Liu, Y. (2016). Industry 4.0 Development and Application of Intelligent Manufacturing. In Proceedings of 2016 International Conference on Information Systems and Artificial Intelligence (ISAI), (pp. 407–410). IEEE. Cheng, Y., Tao, F., Liu, Y., Zhao, D., Zhang, L. and Xu, L. (2013). Energy-aware resource service scheduling based on utility evaluation in cloud manufacturing system. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 227(12), 1901-1915. Chiarini, A. and Kumar, M. (2022). What is quality 4.0? An exploratory sequential exploratory mixed methods study of Italian manufacturing companies. International Journal of Production Research, 60(16), 4890-4910. Chiarini, A. (2020). Industry 4.0, quality management and TQM world. A systematic literature review and a proposed agenda for further research. The TQM Journal, 32(4), 603-616. Chiarini, A., Belvedere, V. and Grando, A. (2020). Industry 4.0 strategies and technological developments. An exploratory research from Italian manufacturing companies. Production Planning and Control, 31(16), 1385-1398. Chibucos, T. R., Leite, R. W., and Weiss, D., L. (2005). Readings in family theory. Sage Publications. Cimino, C., Negri, E. and Fumagalli, L. (2019). Review of digital twin applications in manufacturing. Computers in Industry, Elsevier B.V, 113, 103130. Cohen, Y., Shoval, S., Faccio, M. and Minto, R. (2022). Deploying cobots in collaborative systems: major considerations and productivity analysis. International Journal of Production Research, 60(6), 1815-1831. Craddock, W. T. (2013). How Business Excellence Models Contribute to Project Sustainability and Project Success. In Sustainability Integration for Effective Project Management, (pp. 1-19). IGI Global.