Quality 4.0 social and strategic readiness factors: sufficient and Necessary Condition Analysis.

Depósito de Investigación de la Universidad de Sevilla https://idus.us.es/ This is an Accepted Manuscript of an article published by Taylor & Francis in Total Quality Management & Business Excellence, Vol. 35, n. 5-6, on 2024, available at: https://doi.org/10.1080/14783363.2024.2323185 Rico, J. P., Calvo-Mora, A., Medina-Molina, C., & Alves, H. (2024). Quality 4.0 social and strategic readiness factors: sufficient and Necessary Condition Analysis. Total Quality Management & Business Excellence, Vol. 35, No. 5, 559- 583, https://doi.org/10.1080/14783363.2024.2323185 Abstract Industry 4.0 (I4.0) has given rise to a new stream of research on the future of quality management called Quality 4.0 (Q4.0). In this context, the study analyses the social and strategic readiness factors that act as sufficient and necessary conditions for implementing Q4.0. The methodology used combines (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. The reference sample used in the study is 107 Spanish organisations with EFQM recognition of excellence. The results confirm that Leadership and top management commitment, Q4.0 vision and strategy, Human resource management for Q4.0, Organisational culture and Customer readiness have significant effects on the readiness of an organisation to implement Q4.0. These factors represent sufficient conditions. On the other hand, the factors necessary for implementing Q4.0 would be Q4.0 vision and strategy and Supplier readiness. Finally, GAI shows how two conditions, among both social and strategic factors, are widely shared by organisations to explain a high Q4.0 readiness value. Key words: Quality 4.0; Readiness factors; PLS-SEM; Necessary Condition Analysis, Generalized Analytic Induction 1. INTRODUCTION Quality management as a scientific discipline has stagnated in recent years; innovative models have yet to be proposed, and quality professionals are losing importance in organisational structures (Zonnenshain and Kenett, 2020). I4.0, therefore, presents an opportunity for the quality movement to become a leading force, although it must overcome significant challenges for the quality function (Sureshchandar, 2022). Some of the most critical impacts of the use of new technologies, digitalisation and the massive use of data are on the quality function of organisations (Sony et al., 2020), thus giving rise to the term “Quality 4.0”. For Chiarini and Kumar (2021), Q4.0 redefines the approach to quality management to cope with developments linked to information technologies, increased connectivity and digitisation throughout the supply chain, as well as the use of big data for decision making. 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). Along these lines, 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). This is because it is difficult for organisations to plan and assess whether they are ready to implement Q4.0 (Antony et al., 2023b). 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 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 Q4.0 proposed by Antony et al. (2023b). Specifically, this study aims to achieve the following objectives: 1) Identify sufficient factors to prepare an organisation to implement Q4.0. These factors are analysed in terms of sufficient causality. Thus, a specific factor produces an outcome, although it might 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) technique estimates sufficient conditions (Hair et al. 2019a). Likewise, through GAI, the combinations of conditions that explain the existence of a given outcome will be identified (Ragin, 2023). 2) Identify the factors necessary to prepare an organisation for Q4.0. In this case, a necessary causality implies that the result will not occur without the condition, although its presence does not guarantee the result. These types of factors represent barriers or obstacles, i.e., factors that others cannot compensate (Dul et al., 2020; Richter et al., 2020). For their identification, we use the Necessary Condition Analysis (NCA) technique (Dul et al., 2023). Academic research faces the mismatch between methodology and theoretical assumptions regarding cause and effect achieving a theory-method fit requires employing an appropriate methodology (Linder et al., 2023). Theories that consist of a mixture of necessary and sufficient relations are called embedded necessity theories; and assume that while all factors may contribute to producing the outcome, only a few may be necessary (Richter et al., 2020; Bokrantz and Dul, 2023). To develop and test embedded necessity theories must employ a conventional method of analysis, e.g., structural equation modelling (SEM), together with a method that analyses necessity, ensuring a theory-method fit (Bokrantz and Dul, 2023). This is because traditional quantitative methods such as SEM are unable to analyse the existence of necessary conditions (Bokrantz and Dul, 2023). 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 (Dul, 2021). Therefore, it is crucial to know the factors that should be controlled for the successful implementation of new processes and projects in organisations (Sony and Naik, 2020); or the "must-have" factors when it comes to an understanding how to successfully implement certain management practices (Richter and Hauff, 2022). Thus, before implementing Q4.0, organisations must assess the readiness of their determinants (Antony et al., 2023a). Ignoring necessary conditions in research in a field that theoretically assumes their existence will result in incomplete findings and recommendations (Bokrantz and Dul, 2023). The study's results will contribute to advancing knowledge in the field of Q4.0. More specifically, they will contribute to a better understanding of what Q4.0 readiness factors are and how they can be used as indicators to analyse the extent to which an organisation is ready to implement Q4.0 and assess its level of development or maturity. Furthermore, they can help managers and quality managers differentiate between necessary, sufficient and necessary and sufficient readiness factors. This can undoubtedly guide and support the successful implementation of Q4.0 in organisations and optimise decision-making in this area. In order to meet this objective, the theoretical framework describes the most relevant aspects of I4.0, Q4.0 and establishes the hypotheses concerning the factors of preparation for Q4.0. Next, the methodology followed in the work is described, differentiating between sufficient conditions (PLSSEM, and GAI) and necessary conditions (NCA). Next, the results of this double analysis are presented. Finally, the results are discussed, and the implications, conclusions, and limitations of the study are shown, and lines of future research are proposed. 2. THEORETICAL FRAMEWORK AND HYPOTHESES 2.1. Industry 4.0 Organisations are facing the fourth industrial revolution, called "Smart Industry" or "Industry 4.0", which is generating a new way of organising production based on digitalisation and the massive use of information. Compared to the three previous revolutions, I4.0 integrates digital and social changes to create a safer and smarter working environment (Antony et al., 2023b). I4.0 is based on emerging technologies such as cyber-physical systems, the Internet of Things, cloud computing, additive manufacturing, artificial intelligence, big data, virtual and augmented reality, drones and blockchain (Xu et al., 2018). Thus, this new reality allows companies to be more flexible and to adapt better and faster to the changing needs of the markets (Zonnenshain and Kenett, 2020). I4.0 is enabling the manufacturing and marketing of more customised products, preserving mass production while meeting customer requirements (Sader et al., 2019). However, this can be achieved if organisations can integrate (vertically, horizontally and end-to-end) digital technologies and tools into all processes of their value chain and involve all stakeholders in the supply chain (Sony et al., 2020). Technological advances linked to I4.0 have enabled the implementation of accurate quality prediction frameworks on production lines (Markatos and Mousavi, 2023). Thus, I4.0 offers excellent potential for quality management through the development of real-time continuous monitoring systems, effect prediction systems, intelligent online quality controls and total inspection systems (Sureshchandar, 2022). For Sony and Naik (2020), the application of I4.0 can improve the competitiveness of organisations. However, introducing the technology will undoubtedly pose significant challenges at management, organisational and supply chain levels, as integrating a large amount of data obtained from related value chain activities is necessary (Chiarini et al., 2020). Therefore, Total Productive Maintenance (TPM) is sometimes pointed to as a tool to increase the effectiveness of the relationship between humans and machines (Toke & Kalpande, 2023). TPM helps to reduce losses linked to system failures and is a pillar of quality systems (Kalpande & Toke, 2023). The challenge of I4.0 leads organisations to invest in new technologies, train their employees and adopt new business models that emphasise differentiation through innovation, product customisation and higher quality and excellence (Sánchez-Franco et al., 2023). 2.2. 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; 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. 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 application and massive data use (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). Q4.0 is more than applying technology and digital tools to quality management, with cultural and human elements essential to drive its performance. Since technology is only one of its components (Antony et al., 2023a), Q4.0 goes beyond technologies. Q4.0 is a comprehensive approach to quality management that combines modern technology with proven quality methods to expand the scope of quality management and improve the effectiveness and efficiency of associated tasks (Sader et al., 2022). This definition balances the two approaches currently used to define Q4.0, acknowledging I4.0's contribution to quality management while defining quality as an independent activity carried out by quality professionals outside of those with expertise in technology and data analysis (Sader et al., 2022). Q4.0 broadens the quality focus from a local perspective that aims to exclude defective products or monitor manufacturing processes to a more holistic view with intensive monitoring of all contributing elements of the value chain. I4.0 allows the quality movement to become a leading force (Sánchez- Franco et al., 2023). Among the benefits that the adoption of Q4.0 can generate are increasing the efficiency, innovation and performance of organisations while at the same time generating competitive advantages from higher satisfaction, improved business models and the design of better products and services (Antony et al., 2022). Among the challenges facing Q4.0 is the commitment of managers to invest in technology and to guide organisational strategy towards implementing Q4.0 (Antony et al., 2023a). Therefore, organisations should incorporate Q4.0 into their corporate strategies and modify their leadership approaches to accommodate the new working practices. They should concentrate on developing a workforce with the necessary new skills. A wide range of strategic, cultural, and technological factors are required for transformational change, such as Q4.0, to be successful (Antony et al., 2022). 2.3. Quality 4.0 readiness factors 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) 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 to human resources (commitment, training, skills or rewards), and also 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 1). Figure 1. Research model and hypotheses. 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) H1+ H2+ H3+H4+ H5+ H6+ 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) consider 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 also interact with customers and suppliers, and must define and drive change in the organisation (Zulfiqar et al., 2023). Considering the above, we can pose the following hypothesis: H1: Leadership and top management commitment is a factor that positively affects an organisation's readiness to implement Q4.0. Q4.0 vision and strategy For Dean and Bowen (1994), quality guarantees survival and growth, so it should be considered a primary component of any organisation's strategy. Powell (1995) and Zatzick et al. (2012) go further, considering quality management and improvement as strategic aspect that allows organisations to achieve competitive advantages. According to Oakland (2011), integrating quality and strategic planning into practices and culture contributes to achieving the organisation's mission and vision. Chiarini et al. (2020) point out the need for organisations to design a deliberate strategy for implementing I4.0. Organisational management must clearly understand the benefits and risks of adopting these technologies and have a well-defined plan for implementation (Antony et al., 2023b). For Sony et al. (2021), Q4.0 must be aligned with the organisation's vision and strategy. Thus, for the successful implementation of Q4.0, the organisation must have a clear vision of how technology can help improve the quality of products and services and how this aligns with the strategic objectives. Ultimately, the organisation's vision and strategy are essential for successfully implementing Q4.0. The organisation's management must have an overall vision of how technology can increase the efficiency and quality of processes and must establish clear objectives and a well-defined implementation strategy. Taking the above into account, we can propose the following hypothesis: H2: Q4.0 vision and strategy is a factor that positively affects the readiness of an organisation to implement Q4.0. Human resource management for Q4.0 Undoubtedly, new technologies will make organisations increasingly complex workplaces, and employees must acquire new skills (Sony et al., 2020). Jobs will require an extensive repertoire of knowledge and skills in problem-solving or the application of Q4.0 tools for planning, control and improvement (Calabrese et al., 2021; Zulfiqar et al., 2023). In this environment, the role of workers and quality professionals will present significant challenges (Chiarini and Kumar, 2022). Therefore, Sony and Aithal (2020) indicate that social and human factors are as important as technology for the success of I4.0. For the implementation of Q4.0, the involvement of employees is critical, so it is crucial to motivate them and value their efforts (Sony et al., 2021). Thus, a reward and recognition programme must be based on performance indicators that allow a monitoring of the implementation of Q4.0 and is connected to its quality objectives (Antony et al., 2023b). Furthermore, such recognition and reward systems can boost employee motivation and commitment, foster cooperation and teamwork, and improve the overall standard of work (Chiarini and Kumar, 2022). Considering the above, we can hypothesise the following: H3: Human resource management for Q4.0 is a factor that positively affects the readiness of an organisation to implement Q4.0. Organisational culture towards Q4.0 Culture defines the principles, values, assumptions, and approaches characterising an organisation (Cameron and Quinn, 1999). Organisational culture represents its identity and personality and constitutes how the organisation responds to changing environmental conditions, distinguishing it from other entities (Zonnenshain and Kenett, 2020). An organisational culture encouraging risk-taking, challenge and creativity will help the organisation transition to I4.0 implementation (Sony and Aithal, 2020). For Sony et al. (2020), the four types of culture proposed by Cameron and Quinn (1999) could play an important role in Q4.0. Thus, clan culture promotes teamwork, commitment and participation. Furthermore, the adhocratic culture encourages creativity, risk-taking and innovation. These cultural values are key to creating a context that fosters knowledge management and the massive use of data, key aspects for the successful implementation of Q4.0 (Zulfiqar et al., 2023). For its part, the market culture seeks to differentiate itself from competitors. In this sense, Q4.0 is a strategic issue that seeks to generate sustainable competitive advantages (Sader et al., 2022). Finally, the hierarchical culture's internal focus seeks SR6. Suppliers receive feedback and suggestions from the organisation to use Quality 4.0 principles and tools to improve the quality of their products and services. 0.902 0.201 Readiness for Quality 4.0 (RQ) n.a n.a. RQ1. How prepared is the organisation for Quality 4.0? 1.000 1.000 Notes: CR: composite reliability; AVE: average variance extracted; n.a.: non-applicable. Table 2. Measurement model results Finally, discriminant validity is analysed through the Heterotrait-Monotrait Ratio (HTMT) (Henseler et al., 2015). In this case, there is discriminant validity if the values of the HTMT ratio are below 0.9 and, as shown in Table 3, the model meets the requirement of discriminant validity. Heterotrait-monotrait ratio (HTMT) RC HR LC OC SR VS RQ RC ---- HR 0.791 ---- LC 0.640 0.858 ---- OC 0.806 0.817 0.868 ---- SR 0.746 0.859 0.793 0.833 ---- VS 0.855 0.698 0.601 0.720 0.622 ---- RQ 0.881 0.886 0.806 0.752 0.852 0.843 ---- CR: Customer readiness; HR: Human resource management for Quality 4.0; LC: Leadership and top management commitment; OC: Organisational culture towards Quality 4.0; SR: Supplier readiness; VS: Quality 4.0 vision and strategy; RQ: Readiness for Quality 4.0. Table 3. Measurement model: discriminant validity Structural model The direct relationships between the constructs shown in the study model are shown in Figure 2 and Table 4. We get the t-values and confidence intervals we need to evaluate the statistical significance of the associations using the bootstrapping approach (10,000 samples). We construct a one-tailed Student's t-distribution using hypotheses stating clear, positive correlations between the variables. Additionally, we employ confidence intervals for evaluating hypotheses (Hair et al., 2021). We find that five of the six proposed hypotheses are confirmed using both methods. The predictive power of the structural model reaches a significant value of the explained variance (R2 = 0.888) of the endogenous construct (RQ) (Figure 2 and Table 4). In Table 4, we can assess the effect size (f2), i.e., the degree to which an exogenous (independent) construct contributes to explaining a given endogenous (dependent) construct in terms of R2 (Cohen, 1988). In this sense, the readiness factors Leadership and top management commitment (LC), Q4.0 vision and strategy (VS), Human resource management for Q4.0 (HR), Organisational culture towards Q4.0 (OC) and Customer readiness (CR) show a moderate effect (0.15 ≤ f2 < 0.35). Figure 2. Structural model results. Note: Significance based on t (10,000), one-tailed test. t values in parentheses: t (0.05, 4999) = 1.645; t (0.01, 4999) = 2.327; t (0.001, 4999) = 3.092. *p < 0.05; ** p < 0.01; *** p < 0.001. ns = not significant. Endogenous variable Q2 Exogenous variables Direct effect (path coefficient) t-value (bootstrap) Percentile 90% confidence intervals Support f2 RQ 0.888 LC 0.148** 2.774 (0.085;0.220) Yes 0.154 VS 0.246*** 3.405 (0.159;0.343) Yes 0.263 HR 0.163* 2.154 (0.065;0.291) Yes 0.162 OC 0.218* 2.249 (0.086;0.332) Yes 0.175 RC 0.234*** 3.192 (0.141;0.328) Yes 0.297 SR 0.047ns 0.412 (-0.084;0.212) No 0.002 Note(s): Bootstrapping based on n = 10,000 subsamples. Hypothesised effects resulted from a one-tailed test for a t Student distribution. t values in parentheses: t (0.05, 4999) = 1.645; t (0.01, 4999) = 2.327; t (0.001, 4999) = 3.092 *p < 0.05; ** p < 0.01; *** p < 0.001; ns = not significant. RQ: Readiness for Quality 4.0; LC: Leadership and top management commitment; VS: Quality 4.0 vision and strategy; HR: Human resource management for Quality 4.0; OC: Organisational culture towards Quality 4.0; CR: Customer readiness; SR: Supplier readiness. Table 4. Bootstrap confidence interval through critical value t and intervals 4.2. NCA (necessary causality) NCA establishes the existence of both in kind and in degree necessary conditions. According to Dul (2016), the essential criteria for concluding that a factor is necessary for kind using NCA are: (1) The existence of a theoretical expectation that an antecedent could be a necessary condition for the outcome; (2) An appropriate necessity effect size; and (3) A small p-value of the NCA test statistic demonstrates that the observed effect is unlikely to be a random outcome. NCA allows us to analyse different types of necessary relationships between the condition (X) and the outcome (Y) linked to the "corners" (Dul et al., 2020). In our case, we focus on analysing the Readiness for Quality 4.0 (RQ) R2= 0.888 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) H1= 0.148** H2= 0.246*** H3 = 0.163* H4= 0.218* H5= 0.234*** H6= 0.047ns relationships explained in corner 1, "a high value of X is necessary for a high Y"; and in corner 4, "a low value of X is necessary for a low level of Y". We opted for CR-FDH for calculating ceiling lines because we employ continuous data. Second, the effect size was identified. The effect size can take values between 0 (no space) and 1 (possible space is empty) (Dul et al., 2020). At the same time, four thresholds are initially presented to assess effect size (0≤ d <0.1, small effect; 0.1≤ d <0.3, medium effect; 0.3≤ d <0.5, large effect; d ≥0.5, very large effect), effect sizes greater than or equal to 0.1 are considered relevant (Dul, 2016; Dul et al., 2023). Table 5 shows the effect sizes of the analysed relationships. Effect size(s) Corner 1 Corner 4 CR effect size (p-value) 0.319 (0.000) 0.364 (0.000) c-accuracy 86.1% 85.2% HR effect size (p-value) 0.496 (0.000) 0.285 (0.000) c-accuracy 89.8% 71.3% LC effect size (p-value) 0.396 (0.000) 0.264 (0.000) c-accuracy 91.7% 81.5% OC effect size (p-value) 0.485 (0.000) 0.260 (0.000) c-accuracy 90.7% 80.6% SR effect size (p-value) 0.371 (0.000) 0.246 (0.000) c-accuracy 98.1% 84.3% VS effect size (p-value) 0.269 (0.000) 0.243 (0.000) c-accuracy 98.1% 93.5% CR: Customer readiness; HR: Human resource management for Quality 4.0; LC: Leadership and top management commitment; OC: Organisational culture towards Quality 4.0; SR: Supplier readiness; VS: Quality 4.0 vision and strategy *p < 0.05 Table 5. NCA ceiling accuracy and effect sizes Third, the observed effect size's unpredictability is evaluated using the NCA permutation test. P- values higher than 0.05 in effect sizes cast doubt on their significance (Dul et al., 2020). According to Dul (2016), the accuracy rate of a ceiling line is calculated by dividing the number of observations above or below the line by the total number of cases multiplied by 100. A low accuracy suggests that the ceiling line might need to accurately depict how space is divided. A threshold of 95% is accepted as a benchmark to measure the quality of the generated solution because determining the optimal ceiling line typically includes making a trade-off between the size of the empty area and the number of observations in the empty area (Dul, 2016; Richter et al., 2020). Corner1 Condition SR Condition VS Corner4 Condition VS Figure 3. NCA ceiling lines 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 (effect-size=0.371, p-value=0.000, c-accuracy=98.1%) and VS is necessary in corners 1 (effect-size=0.269, p-value=0.000, c-accuracy=98.1%) and 4 (effect-size=0.243, p-value=0.000, caccuracy=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 3 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". 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 6). 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 6. Bottleneck table (percentages) 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. 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 7). 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: Organisational 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 7. Models for high Quality 4.0 readiness As seen in Table 7, 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 8). 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: Organisational 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 8. Models with denial of a high degree of Quality 4.0 readiness 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 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). 5. DISCUSSION AND IMPLICATIONS As Figure 2 and Table 4 show, except Supplier readiness (SR), all the Q4.0 factors identified in the literature positively and significantly affect the organisation's Q4.0 readiness implementation, i.e., they represent sufficient conditions. Therefore, an increase in the level of the factors Customer readiness (CR), Human resource management for Q4.0 (HR), Leadership and top management commitment (LC), Organisational culture towards Q4.0 (OC) and Q4.0 vision and strategy (VS) will increase the degree of readiness for Q4.0 (RQ). Furthermore, as can be seen from the values of f2 (Table 4), the factors that contribute most to explaining the degree of readiness for Q4.0 are Customer readiness (CR), Q4.0 vision and strategy (VS) and Organisational culture towards Q4.0 (OC). On the other hand, in Tables 5 and 6, as well as in Figure 3, we observe that Supplier readiness (SR) and Q4.0 vision and strategy (VS) represent necessary conditions, i.e., these factors must be present for the organisation to be considered ready for Q4.0. In other words, their absence impedes or hinders implementing Q4.0. Furthermore, we note that Supplier Readiness (SR) is a necessary factor, but it is not a sufficient condition. This means that a certain level of this factor is necessary for an organisation to be ready for Q4.0. However, increasing the factor levels will not increase the organisation's Q4.0 readiness. It is also noteworthy how the Q4.0 vision and strategy (VS) factor is both a necessary and sufficient condition. This means that an increase in this factor will increase the organisation's Q4.0 readiness and that a certain level of this factor is necessary to be ready to implement Q4.0. A summary of the results is presented in Table 9. Readiness factors PLS-SEM results NCA results Implication • Quality 4.0 vision and strategy (VS) It is a significant factor (sufficient condition) It is a necessary condition • An increase in this factor will increase readiness for Quality 4.0. • A certain level of this factor is necessary for the organisation to be ready for Quality 4.0. • Customer readiness (CR) • Human resource management for Quality 4.0 (HR) • Leadership and top management commitment (LC) • Organisational culture towards Quality 4.0 (OC) These are significant factors (sufficient condition) These do not represent necessary conditions • An increase in these factors will increase readiness for Quality 4.0. • Each level of these factors is optional to ensure that the organisation is Quality 4.0 ready. • Supplier readiness (SR) It is not a significant factor (not a sufficient condition) It is a necessary condition • For an organisation to be ready for Quality 4.0, a certain level of this factor is necessary • An increase in the factor will not further increase the organisation's readiness for Quality 4.0. Table 9. Situations relevant to the interpretation of the results On the other hand, through GAI analysis, we identified a series of combinations of social and strategic factors that explain the degree of Q4.0 readiness. Thus, the combination of the social factors, Organisational culture towards Quality 4.0 (OC) and Human resource management for Quality 4.0 (HR), and the strategic factors, Customer readiness (CR) and Supplier readiness (SR) lead to a high performance in the degree of readiness for Q4.0 (Tables 8). Moreover, the combination of Customer and Supplier readiness (CR*SR) is interesting as it reflects the differential behaviour of a condition (Supplier readiness) as necessary or sufficient. Thus, since it does not significantly impact on Q4.0 readiness when taken in isolation in PLS, it is not a sufficient condition. However, according to NCA, it is a necessary condition that, combined with Customer readiness, explains achieving a high Q4.0 readiness in the GAI analysis. On the other hand, the combinations of social and strategic factors that explain the failure to achieve a high level of Q4.0 readiness are analysed (Table 9). Thus, the joint presence of not achieving a high value in adequate organisational culture (OC) and human resource management (HR), as well as the negation of the latter with not achieving a high value in management leadership (LC), result, in most cases, in not achieving a high level of readiness. In addition, in a small number of cases, the combined presence of social factors leads to a failure to achieve a high level of preparedness. This situation may be because, in this group of organisations, the strategic factors have acted as a counterweight to the first ones (it should be remembered that the analysis of the different types of factors has been carried out separately). Regarding strategic factors, the simultaneous lack of Q4.0 as a critical strategic issue (VS) and supplier readiness and involvement for Q4.0 lead to a need for higher levels of readiness. As we have pointed out, no research in the literature addresses the identification of must-have and should-have Q4.0 readiness factors. Despite this, the results support the importance of the readiness factors identified in the studies by Sony et al. (2021) and Antony et al. (2023b), which have been taken as a reference in this paper. More specifically, the results show that Q4.0 vision and strategy (VS) is critical for the organisation's readiness for Q4.0. This result is in line with Chianini et al. (2020), Sony et al. (2021) and Kumar et al. (2022) who note the need to design a deliberate strategy for Q4.0. Moreover, as it is a necessary condition for organisations to be ready for the implementation of Q4.0, it exceeds the role of a significant variable attributed to it by Antony et al. (2023a). Concerning VS, we can affirm that beyond having a significant effect on RQ, high values of VS are required for RQ to have high values. Also, their presence combined with low Customer involvement (CR) leads to not obtaining high readiness levels. Regarding Supplier readiness (SR), we note its importance for RQ. Although the presence of SR does not guarantee RQ (it is not a sufficient condition), SR is required to reach a specific value for RQ to be present. In addition, their combination with Customer readiness (CR) leads to high readiness for Q4.0. In this way, SR enables and helps Q4.0 to be implemented in line with the approaches of Sony et al. (2021) and Thekkooke (2022). On the other hand, Leadership and top management commitment (LD) and Human resource management for Q4.0 (HR) significantly impact the degree of readiness for Q4.0. This result is in line with the findings of authors such as Sureshchandar (2022) and Antony et al. (2023b). It also highlights the role of Organisational culture (OC) as a factor that helps the organisation transition towards implementing Q4.0 (Sader et al., 2022). Along these lines, Kumar et al. (2022) also point out how organisations must systematically develop the necessary strategies, competencies and culture to prevent their organisation from being overwhelmed with the implementation of Q4.0 and to transition to digitalisation as smoothly as possible. Finally, as previous works such as Chiarini (2020), Sony et al. (2021) and Antony et al. (2023b) indicate, in order for the organisation to be ready for Q4.0, it must be customer-oriented (Customer readiness -CR). The importance of culture (CO), human resources (HR), and customers (CR) in achieving high degrees of Q4.0 readiness is also confirmed by their presence in combinations of factors that result in a high degree of Q4.0 readiness. Theoretical and management implications Among the theoretical contributions of this paper, 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. This suitability is even more remarkable because one of the necessary conditions is insufficient (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. 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, in order 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. Finally, identifying combinations of social and strategic factors that lead to high and low levels of Q4.0 readiness confirms the need to implement Q4.0 from a systemic point of view, understanding that there are relationships and synergies between the readiness factors. 6. CONCLUSIONS This paper has been developed 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 should-have 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. Limitations and future research lines 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 analyzed, 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 taken into account 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.