Unraveling the complexity of Quality 4.0 technological dimension adoption: a configurational approach using QCA Open Access

The rapid evolution of Industry 4.0 (I4.0) has reshaped industrial landscapes, ushering in an era of technological integration, innovation, and operational excellence (Xu et al., 2021). At the center of this transformation lies Quality4.0 (Q4.0), a paradigm that redefines traditional quality management systems (QMS) through advanced digital technologies such as artificial intelligence (AI), machine learning, the Internet of Things (IoT), and big data (Antony et al., 2023a). As the Fourth Industrial Revolution advances—marked by digitalization and interconnectivity—Q4.0 has emerged as a flexible and adaptive framework that leverages real-time data and digital tools to revolutionize quality management (QM) (Chiarini, 2020).

However, adoption remains difficult as many organizations struggle to integrate these technologies into existing systems (Antony et al., 2024b). This challenge is acute in sectors such as aerospace, healthcare, and automotive manufacturing, where precision and compliance are critical (Nenadál et al., 2022). Implementation barriers include limited digital infrastructure, cybersecurity concerns, resistance to change, skill shortages, and misalignment with traditional quality practices (Sony et al., 2021; Antony et al., 2024a; Calvo-Mora et al., 2025b).

The COVID-19 pandemic accelerated digital transformation, exposing the limits of conventional QM and intensifying reliance on digital tools (Antony et al., 2022). Remote work and the need for adaptability heightened interest in Q4.0, bringing both opportunities and challenges (Calvo-Mora et al., 2025a). Understanding the key factors enabling effective Q4.0 implementation has thus become essential (Sony et al., 2020; Rico et al., 2024).

Although I4.0 technologies have attracted growing academic attention, research and industrial perspectives on Q4.0 remain limited (Zonnenshain and Kenett, 2020; Maganga and Taifa, 2023a). Most studies emphasize the technical dimensions of I4.0, overlooking its integration into QMS (Sahu et al., 2025). Yet technological readiness is only one element of a broader challenge. Successful Q4.0 implementation also depends on cultural alignment, strategic vision, and leadership commitment—areas still insufficiently addressed in current literature (Sader et al., 2022; Antony et al., 2023b; Rico et al., 2024).

Sector-specific challenges further complicate Q4.0 adoption. The aerospace industry—focus of this study—is marked by high technical complexity, strict regulation, and hierarchical subcontracting. It also faces barriers to digitalization and the integration of advanced technologies such as AI, IoT, and additive manufacturing (Guyon et al., 2019). Ensuring highly reliable, compliant products intensifies pressures on production systems to adapt while maintaining traceability and responsiveness (Pop et al., 2023).

Recent research underscores I4.0’s potential to enhance competitiveness through knowledge management, integrated digital ecosystems, and process redesign based on dynamic capabilities (Ettaibi and Mokhtari, 2021). At the same time, disruptive technologies are reshaping market structures, compelling firms to rethink business models, supply chains, and innovation strategies. Investigating the determinants of technology adoption in aerospace thus addresses theoretical gaps and offers practical insights for transitioning toward smarter, more sustainable, and resilient production models.

Nevertheless, existing contributions have yet to fully capture the multidimensional complexity of this phenomenon. In this regard, research gaps highlight an urgent need for a comprehensive analysis that extends beyond technological readiness to include strategic and cultural dimensions. A deeper understanding of the interaction among these factors is crucial for developing actionable frameworks capable of guiding organizations through the complexities of Q4.0 adoption (Antony et al., 2024b; Calvo-Mora et al., 2025a).

In line with this objective, the present study seeks to identify and model the factors that explain the acceptance and successful implementation of the technological components of Q4.0. By synthesizing insights from the existing literature with empirical findings, this research aims to develop a comprehensive framework that integrates the barriers to Q4.0 implementation with social, cultural, and strategic predictors. Furthermore, it seeks to offer practical recommendations that enable organizations to enhance their capacity for effectively implementing the technological dimension of this transformation. Specifically, we propose the following research questions:

This study makes significant theoretical and practical contributions to the field of Q4.0. It extends existing frameworks by incorporating underexplored dimensions such as leadership, organizational culture, and stakeholder alignment—including employees, customers, and suppliers—all of which are essential for the successful adoption of Q4.0 (Maganga and Taifa, 2023a; Zulfiqar et al., 2023; Nguyen et al., 2024). These dimensions critically shape organizations’ capacity to implement the technological aspects of Q4.0. The study’s sector-specific focus also provides valuable insights for industries facing unique challenges in integrating Q4.0 principles into their QMS (Joshi et al., 2024).

To address these questions, the study employs Qualitative Comparative Analysis (QCA) a method suited to exploring configurational complexity and causal patterns in organizational settings (Fiss, 2011). In fact, the literature has barely addressed how combinations of QM components explain superior performance (Ahsan and Ullah, 2025). Complementary case studies and in-depth interviews enhance contextual understanding and ensure methodological triangulation (Yin, 2018). The sample comprises organizations at varying levels of Q4.0 maturity, ensuring representative and transferable results (Haffar et al., 2020).

The manuscript proceeds with a review of Q4.0 fundamentals, followed by methodology, empirical results, theoretical and practical implications, and concluding reflections on contributions, limitations, and future research directions.

This study integrates four complementary lenses to explain Q4.0 technological adoption: socio-technical systems theory, dynamic capabilities theory, the technology–organization–environment (TOE) framework, and Diffusion of Innovations (DOI) theory. Together, these perspectives capture (1) the joint optimization of technology and social systems, (2) strategic sensing–seizing–reconfiguring processes, (3) contextual organizational and environmental constraints, and (4) diffusion dynamics driven by perceived innovation attributes.

Socio-technical systems theory, introduced by Trist and Bamforth (1951) and further developed by Emery and Trist (1965), highlights the interdependence between technological and social subsystems. In Q4.0, it emphasizes that technological readiness must be accompanied by cultural adaptability and organizational commitment to ensure success (Antony et al., 2022; Maganga and Taifa, 2023c). General systems theory (von Bertalanffy, 1968) and its application to organizations (Katz and Kahn, 1978) extend this view by portraying organizations as dynamic, interconnected systems. Subsystems such as IoT, AI, and big data must be aligned with strategic intent and organizational culture to enhance performance (Maganga and Taifa, 2023b; Armutcu et al., 2025; Calvo-Mora et al., 2025a). Real-time feedback mechanisms further enable continuous improvement (Chiarini, 2020). At the same time, synergy across digital, cultural, and strategic dimensions strengthens organizational capabilities (Sony et al., 2020; Antony et al., 2023b). However, this interconnectedness may also expose vulnerabilities, including infrastructure weaknesses and resistance to change (Orlikowski and Scott, 2008; Nenadál et al., 2022). Equifinality (Katz and Kahn, 1978) further illustrates that similar outcomes can be achieved through diverse configurations, depending on technological, strategic, and cultural readiness (Haffar et al., 2020). Some firms invest heavily in AI and IoT, while others progress through incremental developments or strategic partnerships (Chiarini, 2020; Joshi et al., 2024).

Dynamic capabilities theory, formulated by Teece et al. (1997) and refined by Eisenhardt and Martin (2000), focuses on a firm’s ability to sense opportunities, seize innovations, and reconfigure its resources in a changing environment. In Q4.0, it highlights the importance of adaptive processes (Winter, 2003; Teece, 2007; Chiarini, 2020). Strong dynamic capabilities enable firms to integrate advanced digital tools into quality decision-making while reconfiguring structures, routines, and skills accordingly (Haffar et al., 2020; Sony et al., 2020; AlKhader et al., 2023). Leadership is critical for building such capabilities and aligning technology investments with strategic priorities (Teece, 2018; Nenadál et al., 2022). The sensing–seizing–transforming cycle is particularly relevant in regulated sectors like aerospace and healthcare, where digital innovation must meet strict safety and quality standards (Pop et al., 2023). Ultimately, this theory contributes a strategic perspective on Q4.0 implementation, stressing continual adaptation, leadership, and alignment with long-term goals (Chiarini, 2020; Antony et al., 2024b).

The TOE framework, developed by Tornatzky and Fleischer (1990), examines how technological, organizational, and environmental factors collectively influence technology adoption. It is particularly suited to Q4.0 given its multidimensional scope. Technological factors include infrastructure compatibility, complexity, and integration, reflected in the sophistication of AI, IoT, and big data platforms (Zhu et al., 2006a; Sony et al., 2020). Organizational factors encompass leadership, resources, and innovation culture (Haffar et al., 2020; Antony et al., 2023a), while environmental ones include regulations, competition, and market demands. In regulated industries, environmental forces—such as compliance requirements, customer mandates, and certification regimes—shape both the feasibility and pace of Q4.0 adoption (AlKhader et al., 2023; Antony et al., 2024b). The TOE framework situates Q4.0 adoption within a broader ecosystem of internal and external forces, showing that success relies on the alignment of all three domains.

Rogers’ (2003) Diffusion of Innovations (DOI) theory offers a complementary lens for understanding Q4.0 adoption by explaining how innovations spread within and across organizations over time. In the Q4.0 context, technologies such as AI-driven analytics and IoT-enabled monitoring are more likely to be adopted when managers perceive them as delivering clear performance gains and aligning with existing quality management processes (Zhu et al., 2006b). DOI further distinguishes between early adopters—characterized by experimentation, leadership commitment, and innovation orientation—and late adopters, who tend to exhibit structural inertia and risk aversion. By integrating DOI, this study extends its analytical scope beyond structural and capability-based explanations to encompass the perceptual and diffusion dynamics that are particularly prominent in regulated sectors such as aerospace, where adoption is shaped not only by organizational capabilities but also by perceived legitimacy, risk tolerance, and competitive pressures.

Taken together, these perspectives suggest that Q4.0 technological adoption is shaped by (1) organizational and human readiness (socio-technical systems), (2) strategic alignment and reconfiguration capacity (dynamic capabilities), (3) internal and external contextual constraints (TOE), and (4) perceived innovation attributes and diffusion dynamics (DOI). Hence, we conceptualize Q4.0 adoption as a configurational outcome explained by four condition sets: social readiness, strategic readiness, digital culture, and barriers to implementation.

Q4.0 research has evolved from technology-centric approaches toward multidimensional frameworks that incorporate strategic, cultural, and social conditions. While early studies emphasized the technical benefits of digital tools for real-time quality assurance (Sony et al., 2020), more recent contributions have incorporated factors such as leadership, organizational adaptability, and stakeholder engagement (Maganga and Taifa, 2023a; Rico et al., 2024). This shift has led to the development of frameworks that assess organizational readiness for Q4.0 adoption, highlighting the need to align technological investments with corporate strategy and internal culture (Nenadál et al., 2022). Nevertheless, limited evidence explains how these dimensions combine to produce adoption or non-adoption across contexts. This study addresses that gap by examining how social readiness, strategic readiness, digital culture, and implementation barriers interact to shape the technological dimension of Q4.0 (Antony et al., 2024a).

Q4.0 cannot be understood through a purely technological lens. It is, above all, a sociotechnical system in which human and organizational factors play a decisive role. According to Antony et al. (2023b), the successful implementation of Q4.0 requires the co-evolution of technologies and human capabilities. In this regard, empirical studies such as those by Nenadál (2020) and Rico et al. (2024) have confirmed that factors such as leadership and human resource management directly influence an organization’s readiness for Q4.0. Similarly, Zulfiqar et al. (2023) identify top management commitment, leadership capabilities, and employees’ technical and transversal competencies as critical factors.

Leadership and the active involvement of top management emerge as sine qua non conditions for enabling digital transformation with a quality focus. Leadership not only ensures strategic and financial backing but also fosters a culture of change that legitimizes and drives transformation efforts (Antony et al., 2023b; Rico et al., 2024). This is coupled with the need for strategic management of human talent: while digitalization reshapes the role of the workforce, it does not eliminate it. Technical proficiency, critical thinking, adaptability, and continuous learning become essential skills for effective performance in interconnected environments (Antony et al., 2022). As such, social factors must be understood as foundational components of a Q4.0 strategy. Their absence undermines the viability of any meaningful transformation in QM.

The effective implementation of Q4.0 requires an integrated strategy that coherently aligns technology, organizational structure, and purpose. A dedicated strategic vision for Q4.0 thus becomes a cornerstone of the transformation process, serving as a driver for aligning technological capabilities with business objectives. Rico et al. (2024) argue that such a vision is a necessary condition for achieving adequate organizational readiness. Similarly, Antony et al. (2023b) warn that the absence of a clear strategy constitutes a critical barrier that can derail digitalization efforts if not addressed through deliberate planning and visionary leadership.

This strategic dimension also encompasses alignment mechanisms to ensure coherence between Q4.0 initiatives and existing organizational capabilities (Teece, 2018; Haffar et al., 2020). Moreover, the active involvement of external stakeholders—particularly customers and suppliers—plays a fundamental role. Customers contribute valuable data for quality prediction and personalization, while technologically aligned suppliers enable the development of more agile and efficient collaborative networks (Antony et al., 2023a; Zulfiqar et al., 2023). As noted by Rico et al. (2024), suppliers’ technological readiness is a necessary factor for successful implementation. Therefore, the transition toward Q4.0 must be understood as part of an interdependent ecosystem in which strategic coordination and collaboration are essential.

Beyond technology and strategy, the implementation of Q4.0 requires a profound cultural transformation. Organizational culture—defined as the shared set of beliefs, norms, and values—plays a decisive role in an organization’s ability to adopt and integrate new digital practices. The absence of a culture supportive of change and digital innovation is one of the main barriers to Q4.0 implementation (Rico et al., 2024; Calvo-Mora et al., 2025a).

Culture functions as a moderating variable in Q4.0 implementation outcomes. It influences employee engagement, change management effectiveness, and willingness to innovate (Sony et al., 2020; Orero-Blat et al., 2025). Cultural attributes that facilitate digital transformation include openness to innovation, institutionalized learning, and cross-functional collaboration (Maganga and Taifa, 2023a).

These elements operate at both formal and informal organizational levels, creating a multilevel system of influence that may accelerate or hinder adoption (Haffar et al., 2020). The required cultural transformation goes beyond superficial adjustments; it entails a deep revision of mental models, organizational values, and reward systems. The concept of digital cultural intelligence has emerged as a critical organizational capability for integrating digital technologies without undermining the core principles of QM (Orero-Blat et al., 2025). Achieving this requires the development of digital competencies, the establishment of psychologically safe environments, and the promotion of experimentation and learning from failure (Sureshchandar, 2023; Sahu et al., 2025).

Technological factors represent the structural foundation of Q4.0, as they enable systems integration, advanced data analytics, and the intelligent automation of processes. The ability to make real-time, data-driven decisions and to leverage advanced analytical tools allows organizations to anticipate problems, optimize operations, and generate sustainable value (Thekkoote, 2022; Sahu et al., 2025).

The model developed by Sureshchandar (2022) identifies data competency and analytical thinking as key dimensions of Q4.0, emphasizing that technology does not replace traditional quality approaches but rather enhances them. Zulfiqar et al. (2023) and Armutcu et al. (2025) reinforce this notion, highlighting that the effective management of big data is one of the most highly valued factors among quality professionals in digital contexts. In line with this, Rico et al. (2024) and Calvo-Mora et al. (2025a) identify the strategic use of data as one of the most predictive elements of technological maturity in quality implementation.

Yet, the adoption of technology is neither automatic nor linear. Antony et al. (2022) and Maganga and Taifa (2023c) point to significant differences across sectors and company sizes, noting that many small and medium-sized enterprises (SMEs) lack the infrastructure, skills, or knowledge needed to fully capitalize on the potential of Q4.0. Therefore, technological factors must be considered within a comprehensive strategy that integrates technical capabilities, analytical competencies, and organizational vision. Within this framework, technology is no longer an end, but a coordinated means of enabling more predictive, flexible, and value-oriented QM.

The implementation of Q4.0 faces a range of complex barriers that extend beyond the technological domain. First and foremost is the significant upfront investment required to acquire and integrate advanced technologies, which generates uncertainty around return on investment—particularly for small and medium-sized enterprises (Calvo-Mora et al., 2025b). This economic barrier is compounded by the absence of clear regulatory frameworks and operational standards to guide implementation, hindering adaptation across diverse productive sectors (Roy Ghatak and Garza-Reyes, 2024).

From an organizational perspective, various studies highlight structural rigidity, resistance to change, and the lack of a clear digital strategy as factors that constrain organizations’ ability to respond effectively to Q4.0 challenges (Swarnakar et al., 2025). The shortage of digital competencies among employees, combined with insufficient training and development programs, undermines the workforce’s ability to adopt and adapt to emerging technologies (Sony et al., 2021). Moreover, limited engagement from top management—and the tendency to treat Q4.0 as a purely technological initiative rather than a strategic imperative—weakens the momentum of organizational transformation processes (Calvo-Mora et al., 2025b). External environmental conditions also impose constraints, including regulatory instability, low sectoral standardization, and the absence of robust public policy support for digital transformation—challenges that disproportionately affect emerging economies (Roy Ghatak and Garza-Reyes, 2024).

Ultimately, Q4.0 implementation cannot be reduced to the mere incorporation of digital technologies. It requires a systemic organizational reconfiguration. Corporate culture, change management, strategic vision, and human capabilities act as foundational preconditions for realizing the transformative potential of digital tools. Without this systemic alignment, Q4.0 risks being implemented in a fragmented manner, thereby undermining its promise of operational excellence and sustainable competitive (Sureshchandar, 2022; Antony et al., 2024a).

In recent years, various studies have developed maturity models to evaluate the implementation of Q4.0, though no consolidated approach exists. Thus, Glogovac et al. (2022) use the ISO 9004:2018 standard as a reference to assess the maturity of Q4.0, empirically demonstrating that dimensions such as organizational context, leadership, and process management are fundamental organizational conditions that enable progress towards higher levels of implementation. Nenadál et al. (2022) propose a specific model comprising 22 items and 7 maturity levels to measure organizational readiness for the digital transformation of QM. Maganga and Taifa (2023b) propose an assessment approach focused on technological and analytical capabilities, while Calvo-Mora et al. (2025a) integrate strategic, cultural, and technological dimensions into an empirically validated framework. Thus, these works agree that maturity in Q4.0 goes beyond mere technological adoption and requires a coherent integration of leadership, culture, processes, and digital capabilities. However, it is not yet clear how the combination of these factors can lead to varying levels of success in implementing Q4.0.

In short, although existing maturity models provide valuable frameworks for assessing the level of Q4.0 development, they tend to analyze each dimension separately and assume that their effect accumulates directly, automatically leading to a higher level of maturity. However, it is unlikely that digital transformation in QM depends on isolated factors; rather, it may arise from different combinations of strategic, technological, and social conditions. Therefore, beyond stage-based maturity assessments, an approach is needed to identify the multiple causal combinations of factors that can lead to successful technology adoption within the Q4.0 framework.

Based on the theoretical foundations and existing literature, we propose a research model that examines the key factors and dimensions influencing the successful adoption of the technological component of Q4.0. The model integrates social, strategic, and cultural dimensions, along with barriers to Q4.0 implementation (Figure 1).

The recognition that theories may assign a necessary role to certain variables or conditions in explaining an outcome is consistent with the so-called embedded necessity theories. Several studies have demonstrated that the adoption of Q4.0’s technological factors requires prior organizational readiness, including committed leadership, strategic vision, change management, digital culture, and appropriate human competencies. For instance, Rico et al. (2024) identify strategic vision and supplier preparedness (strategic factors) as necessary conditions for Q4.0 implementation. Similarly, studies by Sureshchandar (2022), Thekkoote (2022) and Talaie et al. (2024) reinforce the idea that successful technological adoption depends on a robust organizational structure, with culture, leadership, and training serving as foundational pillars of change. In this context, digital culture and openness to change act as catalysts that amplify the positive effects of technology.

Furthermore, Antony et al. (2024a) identify a set of “critical failure factors” in Q4.0 implementation, highlighting resistance to change, lack of training, limited understanding of the Q4.0 concept, and the absence of strategic alignment as key causes. These non-technological barriers can lead to stagnation or failure of technological initiatives, even when the necessary digital infrastructure is in place. This argument is supported by findings from Calvo-Mora et al. (2025a), who conclude that organizations with underdeveloped quality culture, leadership, and knowledge management capabilities fail to achieve high levels of technological implementation, which results in reduced operational performance and lower innovation capacity. Finally, studies such as those by Zulfiqar et al. (2023) and Maganga and Taifa (2023b) demonstrate that technological success or failure is closely linked to social factors such as internal knowledge, executive commitment, and stakeholder engagement—whose absence can compromise the entire digital quality architecture.

In light of the above, we formulate the following propositions:

The notion that technological adoption is not the result of a single cause but of complex configurations of contextual conditions aligns with the theoretical principles of the configurational approach. In this regard, Rico et al. (2024) demonstrate that the successful implementation of Q4.0 occurs when multiple factors—such as organizational culture, leadership, and human resource management—are present simultaneously. These findings point to synergistic interactions rather than independent influences, indicating that no single factor alone can fully explain success. Likewise, studies such as Sahu et al. (2025) emphasize that strategic leadership, quality culture, and technological scalability operate together as influential dimensions whose interaction is key to technological adoption.

Conversely, Antony et al. (2024a) reveal that the combined absence of strategic vision, cultural preparedness, and technical training creates an organizational environment resistant to digital transformation. In fact, these factors interact cumulatively, generating a spiral of resistance, uncertainty, and technological misalignment, ultimately leading to adoption failure. Calvo-Mora et al. (2025a) find that organizations in the early stages of Q4.0 adoption not only lack technological capabilities but also exhibit deficiencies in leadership commitment, organizational culture, and data integration. Thus, technological failure is not solely attributable to insufficient digital investment, but to the confluence of multiple interacting social and strategic factors. Finally, studies by Maganga and Taifa (2023b) and Zulfiqar et al. (2023) show that organizations that successfully adopt Q4.0 tend to combine advanced technologies with a collaborative culture, a clear strategy, and effective change management. In contrast, those that fail often exhibit simultaneous weaknesses across several of these dimensions.

In line with the above, we formulate the following two propositions regarding combinations of sufficient conditions:

The proposed research model and propositions offer a theoretical framework for examining the success of Q4.0 implementation, while acknowledging the complex interplay among implementation barriers and technological, social, strategic, and cultural factors. This framework advances current understanding of Q4.0 adoption and offers practical insights for organizational leaders navigating digital transformation initiatives.

Different perspectives and methodologies have been used to establish the dimensions that characterize Q4.0 (Calvo-Mora et al., 2025a), sometimes applying terminology close to the logic of necessity and sufficiency. Thus, from a perspective very close to necessity, readiness factors are considered essential ingredients (Antony et al., 2022, 2023a, b; Sony et al., 2021), or enablers for the effective application of QM. Thus, it is stated that EFQM enablers (leadership, partnership and resources, people, processes, strategy and policy) are necessary conditions for achieving a minimum level of TQM (Oliveira and Gomes, 2025). On other occasions, from a sufficiency-aligned perspective, critical factors or dimensions of Q4.0 are discussed, focusing on elements that need reinforcement to improve the effectiveness of Q4.0 implementation (Sony et al., 2020; Sahu et al., 2025).

The application of configurational techniques has been advocated within the QM domain (Mas-Machuca et al., 2021; Kwasi-Acquah et al., 2023; Rico et al., 2024). Among these, QCA is frequently employed for analyzing multiple cases, serving as a tool to simplify and synthesize qualitative findings in quality research (Mas-Machuca et al., 2021). In fact, while Rico et al. (2024) identify the must-have and should-have factors for implementing Quality 4.0, QCA can be applied to explain the complex relationships and interactions among these factors, thereby explaining the success of TQM implementation (Ahsan and Ullah, 2025). In this sense, QCA has clarified the complex, non-linear, and synergistic effects of TQM practices, which are sufficient to explain improvements in operational results (Acqua et al., 2023). QCA offers an additional perspective that enables explanation of how the combination of component dimensions of quality yields a specific result (Gaudenzi et al., 2021). By offering insights into the intricate, synergistic, and non-linear effects of Total Quality Management (TQM) practices, QCA uncovers sufficient and complex configurations that lead to operational results (Kwasi-Acquah et al., 2023). This methodological approach thus provides a valuable lens for understanding multifaceted causal relationships in QM research. They use QCA to identify the combinations of success factors that explain the achievement of a high result (Ahsan and Ullah, 2025).

This study adopts a qualitative approach based on interviews and closed-ended surveys conducted with executives from the Spanish aerospace sector, which allows for the capture of in-depth perceptions about the factors that influence the adoption of technologies associated with Q4.0, as well as an understanding of the organizational dynamics that affect their implementation. Following the studies by Antony et al. (2023b) and Sony et al. (2021), the research aimed to access empirical knowledge based on the direct experience of people actively involved in the digital transformation of their organizations.

Participants were selected through simple random sampling using several specialized industry directories, including the records of the Spanish Aviation Safety Agency (AESA) and the Federation of Metallurgical Industry Entrepreneurs (FEDEME). This sampling strategy was intended to ensure broad representation of the sector and avoid the bias associated with the intentional selection of companies that are particularly advanced in their digitalization processes. To improve clarity and ensure participants’ understanding of the interview guide, face-to-face interviews were prioritized, in line with the methodological recommendations of recent studies on Q4.0 (Antony et al., 2023b). Data collection was carried out during the last quarter of 2024. Each interview lasted an average of 70 min, allowing for in-depth exploration of the barriers and social, technological, and strategic dimensions of Q4.0 implementation.

The sample consists of 17 companies and includes the following: six manufacturers of aerospace components, five engineering firms, two metrology laboratories, two aircraft manufacturers, two producers of aerospace products, two maintenance service providers, and one company offering auxiliary services and training. Most interviewees held the position of Quality Director or Quality Manager within their organization, although in 20% of the cases, the interviewee was the CEO or General Director. In three interviews, more than one executive participated.

With regard to company size, nine firms (53%) were large enterprises (250 or more employees), two (12%) as medium-sized (between 50 and 249 employees), and six (35%) as small enterprises (between 10 and 49 employees). Furthermore, nine companies (53%) were in the active development phase of Q4.0, having been implementing such practices for between one and three years. The remaining eight companies (47%) were considered mature in their Q4.0 adoption, with more than three years of implementation experience.

Interviewees were also asked about the possession of industry-relevant certifications. The most commonly held certification was ISO 9001 (88%), followed by ISO 14001 (65%) and ISO 9100 (53%). In contrast, only 24% of the companies held ISO 45001 certification. Additionally, seven companies reported holding other certifications, including ISO 17020, ISO 17021, ISO 27001, PECAL 2110, and various NASCAP accreditations specific to major manufacturers in the aerospace industry.

The final questionnaire was divided into three sections. The first section collected information about the respondent’s position, the size of the organization, the level of Q4.0 implementation, the degree of customer and supplier integration, and the presence of industry-relevant certifications. The second section focused on barriers and the dimensions or factors related to Q4.0 management, including the social and strategic dimension, digital culture, and the technological dimension. Finally, a set of open-ended questions was posed to participants.

The scales used to measure the Q4.0 dimensions were adapted from the studies of Sony et al. (2020), Antony et al. (2023b), Leal-Rodríguez et al. (2023), Sureshchandar (2023), and Zulfiqar et al. (2023) (Table 1) to ensure content validity. A five-point Likert scale (1 = Strongly disagree; 3 = Neither agree nor disagree; 5 = Strongly agree) was used to measure variables representing barriers to Q4.0 implementation as well as social, strategic, and cultural factors. A second five-point Likert scale (1 = Very low level; 3 = Moderate level; 5 = Very high level) was employed to measure the technological dimension.

Due to the overall objective and the RQi set, Qualitative Comparative Analysis (QCA) is the most appropriate data analysis method. Thus, QCA is a method rooted in Set-Theory, particularly well-suited for analyzing causal complexity. It elucidates how different combinations of factors (referred to as conditions) converge to produce a specific outcome. The increasing prominence of QCA in management research is attributed to the applicability of its epistemological foundations. First, QCA identifies necessary and sufficient conditions. Second, the principle of asymmetry highlights that the explanation for a phenomenon and its absence require distinct reasoning. Third, conjunctural causation emphasizes that the combination of conditions drives the occurrence of a specific outcome; furthermore, the effect of any single condition depends on those with which it interacts. Finally, the principle of equifinality underscores that outcomes may arise from multiple combinations of conditions, meaning that various “recipes” can explain the same result.

To aggregate second-order dimensions, this study applies the weakest-link logic (Goertz, 2020), which assigns the lowest fuzzy-set membership score across all relevant components, based on the assumption of non-substitutability and balance among elements (Bazzan et al., 2022; Goertz, 2020; Schrijvers et al., 2024). According to this logic, a second-order construct is only as strong as its weakest component (Filippopoulos and Fotopoulos, 2022). The weakest-link postulate posits that components must be balanced for efficiency and penalizes the weakest component, assuming that any dimension must be present for the underlying dimension to be present. This penalty depends not only on the score of the weakest component but also on the difference between its score and those of the remaining components (Schrijvers et al., 2024). As such, membership scores for second-order dimensions incorporate penalties not only for the lowest-scoring item but also for discrepancies between that score and the others (Schrijvers et al., 2024).

In this study, we adopt a model that explains the Technological Dimension of quality as a function of the Social and Strategic Dimensions, Barriers to Quality 4.0 Adoption, and Digital Culture. These dimensions are composed of the following components:

1. Social Dimension: Top management commitment and support, leadership, and employee competence.

2. Strategic Dimension: Vision and strategy for Quality 4.0, customer involvement, and supplier involvement.

3. Technological Dimension (Outcome): Data-driven decision-making, analytical thinking, advanced analytics, and the adoption of new technologies.

The conditions and outcome were calibrated to reflect the empirical behavior of the underlying variables (see Table 2), using specific anchor points tailored to the analytical objectives of the study.

The first step in the analysis involved identifying the presence of necessary conditions (Table 3). The Social Dimension (Consistency = 0.920, Coverage = 0.738, Relevance of Necessity [RoN] = 0.728) and the Strategic Dimension (Consistency = 0.925, Coverage = 0.686, RoN = 0.645) exceed the accepted thresholds to be considered necessary conditions for the presence of the Technological Dimension. No conditions met the threshold to be classified as necessary for the absence of the Technological Dimension.

To identify the sufficient conditions explaining the presence or absence of the Technological Dimension, both parsimonious and complex solutions were generated, following the approach proposed by Fiss (2011) distinguishing core and peripheral conditions (Table 4). A truth table was constructed using a consistency threshold of 0.85 and one case per configuration. In the sufficiency analysis, a consistency ≥0.8 is accepted as a benchmark (Greckhamer et al., 2018). However, the required level was increased to 0.85, as greater consistency is recommended for smaller sample sizes (Scneider and Wageman, 2013). Likewise, one case per configuration was requested, as this is the level suggested for small-N QCA, as in the present case (Greckhamer et al., 2018). The complex solution demonstrated high explanatory power for both the presence of the Technological Dimension (TEC) (inclS = 0.872, PRI = 0.802, covS = 0.818) and its absence (∼TEC) (inclS = 0.950, PRI = 0.914, covS = 0.781). While the presence of the Technological Dimension is explained by three configurations, the absence of the Technological Dimension is explained by four configurations. These findings underscore the intricate, conjunctural relationships among Digital Culture, the Social and Strategic Dimensions, and Barriers in shaping both the presence and absence of the Technological Dimension.

The graphical representation of the solutions (Figure 2) reveals deviant cases that merit closer analysis. Deviant coverage cases, those that exhibit the outcome under study but are not explained by any of the configurations identified in the solution—meaning a different combination of conditions may account for the observed outcome. These cases exhibit the Technological Dimension of Q4.0, but its presence is not accounted for by the identified configurations. Deviant consistency in kind cases meet one of the configurations included in the solution set but do not exhibit the outcome of interest—present the specified combination of conditions but have not adopted the Technological Dimension of Q4.0.

In the case of the presence of the Technological Dimension, a deviant coverage case is Case 11. For the negation of the Technological Dimension, Case 12 emerges as a deviant coverage case. These cases highlight the need for a nuanced interpretation of the solutions, emphasizing the importance of understanding the underlying factors driving such deviations.

In the explanation of the Technological Dimension, two deviant consistency cases were identified (Table 5): Case 12 and Case 13. Conversely, in the explanation of the negation of the Technological Dimension, Case 11 was identified as a deviant consistency case. Particularly notable is Case 13, which, despite having a very high membership score (0.92) within the relevant configuration, does not exhibit the Technological Dimension outcome highlighting the complexity of the relationships between conditions and outcomes, suggesting the presence of additional factors or contextual nuances not captured within the current model.

To control for the identification of false positives, the Braunmoller test was conducted (Table 6). As shown in the table below, the consistency values for the terms under analysis fall outside the confidence interval, rejecting the possibility of identifying false positives. This ensures the robustness and validity of the findings.

The Robustness Test Protocol (Oana et al., 2021; Oana and Schneider, 2024) was conducted using two comparison models that modified the consistency threshold for inclusion in the truth table to 0.8 and 0.9. The results demonstrate optimal Robustness Fit parameters for both the presence of the Technological Dimension and the negation of the Technological Dimension. Additionally, the Robustness Case Ratio revealed acceptable parameters for the presence of the Technological Dimension, while perfect parameters were obtained for the negation of the Technological Dimension.

Furthermore, to assess the applicability of the explanatory solutions across varying conditions, a cluster analysis was conducted using the “maturity of implementation and development of Quality 4.0” as a grouping variable. Maturity levels were calibrated such that firms were classified as 1 (if at levels 1 or 2) or 2 (if at levels 3 or 4). The results indicate that the explanatory solution for the Technological Dimension is generalizable across different groups of firms, regardless of their Quality 4.0 implementation maturity (Table 7).

Likewise, the explanatory results of the denial of the Technological Dimension can also be extrapolated (Table 8).

The findings highlight the central role of strategic and social dimensions as necessary conditions for the adoption of the Technological Dimension of Q4.0 in the Spanish aerospace sector. This supports the view that digital transformation within QMS cannot be approached as a purely technical endeavor. Instead, it requires a robust organizational foundation (Antony et al., 2024b; Sahu et al., 2025).

In this context, Rico et al. (2024) specifically emphasize the importance of having a clearly defined Q4.0 vision and strategy, along with supplier readiness—two elements that, in this study, are subsumed under the broader Strategic Dimension. Additional studies reinforce the relevance of human factors in catalyzing the potential benefits of digital transformation (Antony et al., 2022; Sureshchandar, 2022; Thekkoote, 2022).

Notably, no necessary conditions were found for the absence of the Technological Dimension. This suggests that no single factor, when absent, can independently account for technological non-adoption—further supporting the argument that multi-causal explanations are more appropriate in this domain.

To explore such explanations, the study applied Qualitative Comparative Analysis (QCA) to identify combinations of sufficient conditions that lead to either the presence or absence of the technological dimension. Both complex and parsimonious solutions were generated (see Table 4), with the complex solution offering high explanatory power for both outcomes.

In the case of successful adoption, three distinct configurations were identified:

1. Strategic Dimension * Social Dimension * Digital Culture

2. Strategic Dimension * Social Dimension * Absence of Barriers

3. Strategic Dimension * Digital Culture * Absence of Barriers

A key finding is that the Strategic Dimension appears as a core condition in all three pathways. This underscores its strong causal relevance and aligns with prior literature highlighting the importance of aligning Q4.0 initiatives with broader business strategy (Rico et al., 2024; Talaie et al., 2024; Calvo-Mora et al., 2025a; Sahu et al., 2025). An integrated Q4.0 strategy allows organizations to connect digital transformation with customer value creation and continuous innovation (Antony et al., 2024a).

Digital Culture also emerges as a core condition in two of the three configurations, reflecting the importance of fostering an organizational environment that supports continuous learning, cross-functional collaboration, and openness to innovation (Sony et al., 2020; Thekkoote, 2022). Organizations with strong digital cultures tend to make more effective use of emerging technologies in their quality systems (Calvo-Mora et al., 2025a).

In contrast, the Social Dimension and the absence of barriers appear as peripheral conditions, meaning they are only present in the complex (not parsimonious) solutions, potentially acting as facilitators or modulators rather than core drivers. This is consistent with arguments that emphasize the complementary role of human and cultural factors in technology adoption (Sureshchandar, 2022).

When examining the absence of technological adoption (∼TEC), four distinct configurations emerged:

1. Absence of Strategic Dimension * Presence of Barriers

2. Absence of Strategic Dimension * Social Dimension * Digital Culture

3. Absence of Social Dimension * Strategic Dimension * Absence of Barriers

4. Absence of Digital Culture * Absence of Social Dimension * Presence of Barriers

These pathways suggest that the absence of strategic and social dimensions serves as a core condition in two of the four configurations. This reinforces their structural importance—not only for enabling success, but also for explaining failure. Without a clear strategy and stakeholder alignment, Q4.0 initiatives are unlikely to succeed, even when the technical infrastructure is in place (Sony et al., 2021; Antony et al., 2024a).

Meanwhile, barriers to implementation and digital culture act as peripheral conditions, influencing the outcome in more context-sensitive ways. This supports their classification as modulating factors whose causal impact depends on their interaction with other dimensions (Rico et al., 2024; Antony et al., 2024a, b).

To further assess the explanatory boundaries of the QCA model, the study also identified deviant cases. These outliers point to potential unmeasured variables or contextual factors and illustrate the complexity of technological adoption in Q4.0 environments (Rico et al., 2024).

For instance, Case 11 represents a deviant coverage case: it demonstrates a high level of technological adoption but is not explained by any of the sufficient configurations identified. This company, a small and highly specialized aerospace software engineering firm, is characterized by an agile structure and advanced technical expertise. These attributes may enable fast decision-making and internal capability development, compensating for the absence of strong customer integration or formal strategic alignment. Some firms may achieve adoption through non-traditional trajectories, driven by technical leadership or internal innovation cultures (Antony et al., 2024a; Mittal et al., 2024).

Conversely, Case 12 represents a deviant coverage case at the other end of the spectrum: a large, well-established aerospace firm with low adoption of the technological dimension, despite not matching any of the configurations for non-adoption. Its immature digital infrastructure and limited external integration—combined with possible structural inertia or slow change processes—may account for the discrepancy (Calvo-Mora et al., 2025a). This suggests that even well-resourced firms may struggle to adopt Q4.0 technologies if they face internal fragmentation, misaligned strategy, or resistance to change (Swarnakar et al., 2025).

Furthermore, Cases 12 and 13 qualify as deviant consistency in kind, as both match configurations associated with technological adoption but fail to exhibit the outcome. This suggests the presence of blocking mechanisms not captured by the existing model. In particular, Case 13 shows a high membership score (0.92) in the corresponding configuration, further emphasizing the empirical inconsistency. Both organizations show similar maturity levels (1–3 years) and early-stage customer-supplier integration, which indicates that formal conditions may be in place, but contextual inhibitors—such as resistance to change, lack of transformational leadership, or workforce unpreparedness—could prevent adoption (Sureshchandar, 2022; Calvo-Mora et al., 2025a).

Interestingly, while Case 13 is a small firm, its agility may be undermined by resource and skill constraints, impeding implementation efforts (Sony et al., 2021; Maganga and Taifa, 2023a). In contrast, Case 11, despite being similarly small, benefits from specialization, technical focus, and possibly exposure to global innovation standards, which may act as compensatory enablers in the absence of broader organizational maturity (Antony et al., 2024a; Mittal et al., 2024).

Table 9 reflects the main findings of this work related to the application of a configurational approach through the application of QCA.

This study makes several contributions to the theoretical development of Q4.0, particularly regarding its technological dimension. First, the findings challenge the notion that digital technology adoption follows a linear trajectory. Instead, they support a systemic and configurational perspective consistent with sociotechnical systems theory (Trist and Bamforth, 1951; Orlikowski and Scott, 2008) and organizational frameworks based on equifinality and conjunctural causation (Fiss, 2011). Besides, The integration of Diffusion of Innovations theory further enriches the explanatory framework. While socio-technical systems theory and dynamic capabilities emphasize structural alignment and organizational reconfiguration, DOI introduces the role of perceived technological attributes and innovation orientation. This perspective helps explain why some firms adopt Q4.0 technologies earlier than others, even when structural conditions appear comparable.

Using qualitative analysis, the study identifies both essential factors and effective combinations leading to successful outcomes. Strategic and social dimensions emerge as key enablers of Q4.0 adoption, highlighting the organizational conditions required for digitalization to enhance QM. Notably, digital culture is identified as a core causal element, emphasizing its role not merely as a facilitator but as a structural component of digital transformation within QMS. These findings reinforce the theoretical integration of culture, strategy, and technology (Leal-Rodríguez et al., 2023; Sureshchandar, 2023).

The research also contributes by identifying configurations leading to technological failure. The coexistence of barriers with the absence of strategic guidance or human commitment significantly increases the likelihood of unsuccessful implementation. These insights refine the understanding of critical failure factors and highlight the need for context-sensitive models capable of capturing organizational idiosyncrasies (Antony et al., 2024a).

Methodologically, the study demonstrates the value of Qualitative Comparative Analysis (QCA) in Q4.0 research. The principle of asymmetry reveals that success and failure arise from distinct causal paths. Equifinality allows the identification of multiple viable configurations—three for adoption and four for non-adoption—while conjunctural causation shows that each factor’s influence depends on its combination with others, such as the shifting role of digital culture in failed adoption scenarios. Finally, the analysis differentiates between necessary and sufficient conditions, showing that while some factors are essential for success, none were found necessary for non-adoption.

This study offers valuable practical guidance for aerospace organizations seeking to implement the technological dimension of Q4.0. The findings confirm that technology adoption succeeds only when aligned with structural factors—particularly corporate strategy and social capabilities. Strategic and social dimensions are identified as necessary conditions for adoption, indicating that without a clear strategic vision and an enabling organizational foundation, technological initiatives lack operational viability, regardless of available resources. Practitioners should therefore design Q4.0 strategies aligned with corporate objectives and engage customers and suppliers as part of an integrated digital ecosystem.

The analysis reveals three sufficient configurations for successful adoption, each featuring strategy as a central driver of transformation. Digital culture also emerges as a core condition, fostering innovation, adaptability, and continuous learning. For managers, this highlights the need to cultivate a culture that supports experimentation and collaboration, viewing technology as a natural evolution of work systems rather than an external imposition.

The social dimension and the absence of organizational barriers operate as context-dependent, yet supportive, conditions that create favorable environments for technological integration. Success thus depends on reducing internal resistance, building digital competencies, and framing Q4.0 as a strategic—not merely technical—transformation.

Conversely, non-adoption is strongly associated with the absence of strategic vision combined with structural barriers. Decision-makers should therefore clarify strategic objectives and actively eliminate obstacles to adoption. Finally, deviant cases demonstrate that organizational outcomes may diverge from theoretical expectations: some firms succeed through unconventional paths, while others fail despite favorable conditions. Executives should thus complement standardized frameworks with qualitative approaches—such as contextual analysis and active listening—to better understand the internal and ecosystem dynamics shaping digital transformation.

The first research question in this study concerns the identification of necessary conditions for the success or failure of adopting the technological dimension of Q4.0 within the aerospace industry. The findings indicate that successful adoption of the technological dimension requires the presence of both the social and strategic dimensions. In other words, without these dimensions in place, the technological dimension of Q4.0 is unlikely to be adopted successfully. By contrast, the failure to adopt the technological dimension does not depend on the presence of any single condition considered in this study.

The second research question focuses on the combinations of conditions that explain the successful implementation of Q4.0’s technological dimension. Success is primarily explained by the interaction between the strategic dimension and digital culture, with the social dimension and the absence of barriers playing a less central but still supportive role. Conversely, when explaining failure, the absence of the strategic and social dimensions emerges as particularly critical, outweighing the relative presence of digital culture or partial presence of the other dimensions. In this context, as expected, the presence of barriers acts as a hindering factor, reinforcing its role as a contextual inhibitor to technological adoption.

Despite its theoretical and empirical contributions, this study has several limitations that suggest directions for future research. First, the focus on the Spanish aerospace sector limits the generalizability of findings. Replicating this analysis in other quality-intensive industries—such as automotive, healthcare, or energy—and in different geographical and cultural contexts, particularly in emerging economies, would help assess the transferability of the proposed framework (Antony et al., 2024b; Roy Ghatak and Garza-Reyes, 2024).

Second, although the configurational approach (QCA) enabled the identification of complex causal patterns, the model is constrained by the selected conditions. Some relevant organizational variables were excluded for reasons of analytical parsimony and data availability. Future studies could refine the model by incorporating additional dimensions or disaggregating existing ones to better capture their specific effects.

Finally, while the analysis is based on primary data from semi-structured interviews with senior executives, the results reflect respondents’ perceptions, which may introduce subjective or social desirability bias. Future research should complement these perspectives with objective indicators—such as performance metrics, technology audits, or internal system data—to triangulate findings and enhance empirical validity.