Qualitative study of Q4.0 awareness, challenges, readiness and core skills: a Middle East perspective Open Access

Quality management has been integral to organizational success for decades. It enables firms to meet customer expectations while minimizing defects and waste (Al Shraah et al., 2022). Historically, pioneers such as Shewhart, Taguchi, Smith, Harry, Deming, and Juran developed foundational methods (e.g., TQM, Taguchi methods, DOE, Lean Six Sigma). However, the field has recently called for a paradigm shift in light of Industry 4.0 (I4.0) (Gonzalez Santacruz et al., 2025).

In response, Quality 4.0 (Q4.0) emerged. Q4.0 is the latest iteration on quality management that integrates digital technologies (e.g., AI, IoT, big data analytics) into traditional quality management (Jacob, 2017; Liu et al., 2023). Sader et al. (2022) framed Q4.0 as leveraging advanced technologies and established practices to foster operational excellence. Antony et al. (2023a) highlighted the integration of human judgment and technology to achieve organizational goals. Escobar et al. (2024) further underscored AI's role in managing large datasets and adapting processes. Despite extensive studies in North America and Europe, the Middle East remains comparatively underexamined (Antony et al., 2023a). The Middle East was intentionally selected as the study context due to its accelerated digital transformation agendas (e.g., Vision 2030), unique organizational structures, and the notable absence of region-specific insights in the current Q4.0 literature (Alharbi, 2019). Notably, over 65% of quality professionals worldwide see promise in Q4.0, yet only 16% have begun feasibility assessments (Escobar et al., 2024).

Motivated by this gap, the study proposes four research questions:

1. RQ1. What is the current level of understanding and awareness, and what are the expected benefits and motives for Q4.0 adoption in the Middle East?

2. RQ2. What challenges do firms encounter leading to the failure of Q4.0 initiatives in the Middle East?

3. RQ3. What are the readiness factors for Q4.0 adoption in the Middle East?

4. RQ4. Which core soft and hard skills must quality professionals possess when transitioning to Q4.0 in the Middle East?

To address these questions, the study proposes a conceptual framework explaining how organizational awareness, technological barriers, and human capital needs intertwine within Q4.0 for organizations interested in Q4.0 adoption in the Middle East. This research expands Q4.0 understanding and offers practical recommendations for policymakers, practitioners, and organizations striving for digital excellence in the Middle East. The remainder of this paper is structured as follows: Section 2 examines the relevant Q4.0 literature, Section 3 outlines the methodology, and Section 4 showcases the empirical findings. Section 5 provides comprehensive discussions and implications, whereas Section 6 concludes the study by addressing limitations and proposing future directions.

Researchers have examined the motivations for Q4.0 adoption in developed regions (Antony et al., 2023a). For example, Margherita and Braccini (2024) argued that organizations are drawn to Q4.0 because it enables the development of data-driven Quality Management Systems (QMS), helping to overcome data-related challenges. Sony et al. (2021) identified reliable information sources as a major motive, while Antony et al. (2022) emphasized how data-driven insights facilitate optimal decision-making. Q4.0 also allows the design of adaptive products and services (Salimova et al., 2020), thereby improving stakeholder satisfaction. Antony and Sony (2021) proposed that Q4.0 supports sustainable social, financial, and environmental performance. In addition, it leads to operational improvements, including lower scrap rates, faster time-to-market, and a strengthened culture of innovation (Dias et al., 2022; Gonzalez Santacruz et al., 2025). Antony et al. (2023a) highlighted quicker, more reliable decisions and high Return on Investment (ROI) as pivotal motivations for Q4.0 across European regions. Lastly, Oliveira et al. (2025) mentioned that Q4.0 creates virtual channels that improve communication between humans and machines, resulting in enhanced quality monitoring and operational excellence.

Q4.0 represents a paradigm shift in quality management through real-time monitoring, predictive analytics, and automated decision-making (Javaid et al., 2021), fostering a culture of continuous improvement. However, its adoption poses considerable challenges. Sony et al. (2021) highlighted high capital costs, limited resources, and an entrenched organizational culture as dominant barriers. Moreover, the misconception that merely acquiring technology guarantees successful implementation prompts investments without a clear strategic vision (Antony et al., 2023a). This underscores the necessity of strategic planning to complement technological adoption. Sureshchandar (2023) identified twelve dimensions essential for Q4.0 success, including strategic leadership, a quality-centric culture, customer orientation, competence, and data governance, reinforcing that technology alone is insufficient. Antony et al. (2023a) also emphasized top management's role in aligning Q4.0 with organizational aims to prevent fragmented initiatives. Lastly, Kushwaha and Talib (2025) argued that the fast-paced organizational environment hinders the adoption of disruptive initiatives such as Quality 4.0.

Sader et al. (2022) underscored the importance of robust data infrastructure and strong data-analysis skills as prerequisites for Q4.0, reflecting the centrality of big data to quality enhancement. Escobar et al. (2021) highlighted aligning organizational objectives with Q4.0 capabilities as a major readiness factor. For instance, Maganga and Taifa (2023) stressed that top management and board support are crucial; their absence can derail the entire transformation process. This advocated for an ecosystem-based approach, emphasizing stakeholder collaboration and a well-defined strategy. Antony et al. (2023b) distinguished readiness factors for SMEs from those for large enterprises, finding that top management support is vital in both cases, followed by strategy, leadership, training, awareness, organizational culture, and customer-supplier focus. Resource constraints mean SMEs often prioritize knowledge, culture, and leadership above formal strategy and training. Overall, top management support and a conducive organizational culture consistently emerged as central to Q4.0 readiness (Swarnakar et al., 2025).

Nikolova-Jahn (2019) noted that I4.0 imposes rigorous new demands on quality management. Although Q4.0 leverages cutting-edge technologies, Santos et al. (2021) clarified that the true shift involves changes in leadership, processes, and organizational culture rather than a wholesale replacement of traditional approaches. Accordingly, a solid grounding in established quality management remains essential.

Santos et al. (2021) called for a balanced skill set blending technical competencies, data interpretation, AI, and machine learning, and soft skills, communication, teamwork, leadership, and adaptability. In alignment with this view, Ali and Waheed (2025) proposed that both soft and hard skills are vital for facilitating the transition toward Industry 4.0 and the adoption of Quality 4.0 principles. Ali and Johl (2022) similarly emphasized cultivating a culture of continuous learning and detailed process design, technology proficiency, and data analytics as key hard skills. Antony et al. (2023a) added that digital literacy and data science, combined with teamwork and leadership, are fundamental to Q4.0 success. Figure 1 conceptualizes key aspects of Q4.0.

Despite the growing interest in Q4.0, several significant gaps persist in the current literature. One major gap is the limited understanding of how Q4.0 is perceived and implemented in different regions, particularly in the Middle East. In addition, Digitalization has become a strategic priority in the Middle East, driven by national agendas such as Saudi Arabia's Vision 2030 and the UAE's Digital Government Strategy. These agendas aim to diversify economies, enhance public service delivery, and promote operational excellence through advanced technologies (Nguyen et al., 2024). While existing studies (Sony et al., 2021; Sader et al., 2022; Antony et al., 2023a) have explored the adoption of Q4.0 in developed countries, the unique challenges, motives, and readiness factors specific to the Middle Eastern context remain under-researched. Additionally, there is a lack of comprehensive studies focusing on the core skills required by quality professionals in this region to successfully transition to Q4.0. This warrants more region-specific research that can inform local practitioners and policymakers.

This study employed a qualitative research design, using semi-structured interviews to investigate Q4.0 motives, challenges, readiness factors, and core skills in the Middle East. Qualitative research is well-suited for uncovering deeper insights, especially when participants' lived experiences and subjective interpretations can provide contextual richness (Mahama and Khalifa, 2017). This approach allows for flexibility and the emergence of nuanced responses that quantitative methods might overlook. Ethical considerations, including Institutional Review Board (IRB) approvals, informed consent, and confidentiality, were rigorously upheld throughout the research.

Semi-structured interviews were conducted with quality management experts possessing prior knowledge of I4.0. This qualitative research method facilitated open-ended discussions, enabling participants to share detailed perspectives on complex issues (Creswell and Creswell, 2017). Therefore, a twelve-question open-ended interview protocol was developed and designed to last 40–60 min. The interview protocol was piloted among five experts to ensure uniformity and consistency of questions (Blandford, 2013). Participants were recruited via personalized LinkedIn and email invitations.

A purposive sampling strategy was used, selecting participants who met predefined criteria aligned with the study's objectives. These included (1) managerial roles in quality management, (2) prior knowledge of digitalization and I4.0, and (3) a minimum of five years of professional experience. Fourteen interviews were conducted, offering a diverse representation across industries such as construction, higher education, contracting, aviation, petrochemicals, and logistics. All participants are employed in the Gulf region and have substantial experience working in the Middle East. This cross-sectional selection allowed for exploring commonalities and variations in Q4.0 adoption within the region. Table 1 summarizes the participants' professional profiles.

All interviews were conducted, recorded, and transcribed using Microsoft Teams and analyzed via the constant comparative method shown in Figure 2 (Creswell and Creswell, 2017). The analysis proceeded as follows:

1. Initial coding: a single researcher thoroughly reviewed each transcript, reading them multiple times to gain familiarity. Open coding was then performed to label significant statements related to Q4.0 motives, challenges, readiness factors, and required skills.

2. Refinement and axial coding: The researcher iteratively reviewed the initial codes for similarities, differences, and relationships between categories. Axial coding was then employed to refine and group these codes into broader conceptual categories (e.g., “lack of vision and leadership commitment,” “data availability and integrity,” “IT infrastructure”, etc).

3. Theme development and alignment with research questions: The categories were subsequently mapped to the study's main research questions, forming overarching themes (e.g., “awareness and motives,” “challenges and barriers,” “readiness factors,” “core skills”). Themes were defined based on their prevalence and relevance to Q4.0 adoption in the Middle East.

4. Mitigation of bias: Although one researcher conducted the coding, efforts were made to enhance trustworthiness. First, the coder used a systematic coding guide—developed from the interview protocol—to ensure consistency. Second, member checking was employed by sharing preliminary themes with three participants and seeking their feedback on whether interpretations matched their intended perspectives. This feedback was integrated to refine categories and verify accuracy.

5. Interpretation and findings: The final step involved interpreting how these themes addressed the study's four research questions.

The first theme captures the current awareness and understanding of Q4.0. In addition, this section seeks to report on the knowledge of Q4.0 technologies and the potential benefits and motives for Q4.0 adoption. This analysis encompasses various codes like understanding Q4.0, expected technologies for Q4.0, and perceived benefits and motives for Q4.0.

P(5) underscored the technological essence of Q4.0, noting its potential to enhance data security and thus improve public safety. Meanwhile, P(6) highlighted a broader shift from traditional inspection-focused methods to a holistic approach encompassing suppliers, stakeholders, and shareholders. Furthermore, participants acknowledged that Q4.0 involves harnessing I4.0 technologies such as AI, machine learning, cyber-physical systems, and IoT, complemented by a substantial cultural evolution in quality practices, as demonstrated in the following quotes:

Moreover, P(4) argued that Q4.0 marks the transition of quality management from a proactive to a predictive approach, in alignment with the findings of Escobar et al. (2024). P(9) acknowledged that Q4.0 is more than just inspection and control; it comes with personalized solutions tailored to each organization separately. The following quotes were recorded:

However, many participants, including P(2), P(6), P(10), and P(11), suggested that Q4.0 is still very immature in their respective organizations, as shown in the following quotes:

P(3) noted the usefulness of mobile applications for inspecting and evaluating material quality. Since construction is among the largest industries in the Middle East (Wilson, 2021), Q4.0 holds significant potential to transform quality and safety monitoring. Additionally, P(11) highlighted how IoTs and computer-aided software can lower costs and improve design testing outcomes, thereby enhancing overall quality.

P(4) emphasized the extensive yet underutilized potential for in-process data generation, noting a significant missed opportunity. To address this, P(4) advocated for deploying IoT devices (e.g., sensors) to capture necessary data and employing suitable AI tools for analysis. Similarly, P(8) underscored the vital role of IoT devices in continuous monitoring and recording process variables to produce large datasets.

P(12) noted that real-time tracking systems enable airlines to provide seamless customer experiences by ensuring accurate baggage routing and efficient transfers during connections. This reduces the likelihood of lost or mishandled baggage, a significant source of customer dissatisfaction. Additionally, real-time data on flight status and potential disruptions allow airlines to proactively communicate with passengers through mobile apps, email, or SMS. Table 2 provides a summary of the discussed technologies for service and manufacturing industries.

P(3) highlighted that Q4.0 facilitates real-time monitoring of key properties in building materials and enhances integration with suppliers' processes. Additionally, P(3) noted that Q4.0 enables early notifications of potential failures and the necessity for periodic maintenance, thereby improving operational efficiency and reducing downtime.

Participants identified the generation of dashboard reports as a key motivator for Q4.0 adoption, enabling real-time visualization and decision-making. P(7) stated that dashboards displaying all necessary information and metrics simultaneously could reduce troubleshooting time. Furthermore, P(9) noted that adopting Q4.0 could lead to long-term savings and significant productivity improvements. P(13) observed that in the current dynamic market, Q4.0 equips organizations to identify and respond to market and process changes swiftly, thereby enhancing their ability to manage unforeseen risks and changes (Küpper et al., 2019).

Furthermore, according to P(4), regional organizations are inclined to consider adopting Q4.0 in the coming years due to its anticipated financial advantages. They underscored that these fiscal gains stem from Q4.0's capacity to steer organizations toward achieving their quality goals, underscoring the necessity of aligning Q4.0 initiatives with the organization's mission and vision. Figure 3 illustrates the distribution of potential benefits of Q4.0 across industries.

The second theme addresses the challenges and barriers to Q4.0 adoption in the Middle East. The emerging codes include the lack of vision and leadership commitment to Q4.0, resistance to change and cultural issues, financial difficulties, employees' skill gaps, and hiring challenges.

Participants P(4) and P(12) discussed the need for substantial changes in organizational operations and processes as a major challenge to Q4.0 in the region. They emphasized that major alterations to the organization's vision and goals are essential to align activities with Q4.0, thereby enhancing process efficiency and performance. However, P(12) highlighted the difficulty of shifting an organization's overarching vision and mindset, which often encounters significant resistance from employees entrenched in existing paradigms, impeding the transition to new initiatives. Additionally, P(9) stressed the urgent need to proactively update the company's vision, arguing that this would increase receptivity to change among top-level executives and reduce resistance to innovative initiatives within management.

P(10) highlighted that most organizations in the Middle East are family-owned, with family members developing strong emotional attachments over generations. These deep connections often result in resistance to changes that could disrupt established traditions, values, and operational practices. Additionally, decision-making authority is typically concentrated within the family, where influential members wield significant control. Conflicts, rivalries, and power struggles among family members further complicate the consensus-building process for implementing changes.

Moreover, P(5) noted that top management often lacks up-to-date knowledge, leading to resistance to emerging concepts. P(8) emphasized the significant cultural shift and the need for an open-minded approach to effectively adopt Q4.0, which is particularly challenging for senior employees unaware of rapid technological advancements. Additionally, P(13) highlighted that fostering a culture of continuous learning is essential for Q4.0 adoption, posing a major challenge for top management and leadership.

P(6) argued that many regional organizations engage in superficial technological adoption, investing in technologies without a clear vision or strategic approach. This tendency arises from a desire to project modernity and transformation rather than seek tangible value. P(6) identified this lack of clear objectives and value realization as a major barrier to Q4.0 adoption.

Lastly, participants identified people management as a significant obstacle during transformative initiatives. P(4), P(7), and P(13) emphasized that addressing concerns, communicating the rationale for change, and providing support are crucial for overcoming resistance. Effective management involves engaging employees, seeking their input, and demonstrating commitment to their well-being. This builds trust among employees, which is essential for the implementation of Q4.0. However, organizations view change management as a major challenge and often avoid significant changes to prevent disrupting established workflows (Abdirad, 2022).

P(11) discussed cost and ROI as key barriers to Q4.0 adoption. The participant noted that the absence of Q4.0 in the construction industry complicates the adoption process and limits lessons from other companies' stories. P(2) confirmed that the industry's distinct nature makes cross-sector Q4.0 experiences less applicable. Additionally, P(14) observed that financial considerations significantly hinder Q4.0 implementation across various sectors, though the impact varies by industry.

However, this financial constraint is not always applicable in the region, as many governmental entities and multinational corporations are willing to invest, provided there are quantifiable ROI, clear value propositions, and enhanced quality outcomes. P(6) argued that financial challenges are not the primary impediment, unlike in other developed nations. Instead, the main barrier is understanding Q4.0's benefits and value proposition.

Another major barrier to Q4.0 initiatives in the region, as noted by P(9), is the lack of training programs and curricula covering I4.0 and Q4.0 dimensions. Additionally, P(9) highlighted that leadership often neglects to allocate adequate training time, viewing it as unproductive compared to boosting production or service delivery. Furthermore, P(8) observed that existing training programs are not strategically designed to meet workforce needs. They advocated for a comprehensive approach: conducting thorough assessments of employees' knowledge, categorizing them based on proficiency, and developing tailored training programs aligned with organizational transformation goals.

P(11) noted that most training programs are neither tailored to specific needs nor aligned with the organization's strategic goals. Consequently, employees may perceive them as irrelevant or unnecessary, leading to disengagement.

P(14) indicated that in numerous instances where organizations attempt to recruit personnel to adopt transformative initiatives, the hiring process itself is flawed, resulting in the selection of unqualified candidates. P(14) characterized this scenario as a recipe for failure.

P(3) stated that hiring unqualified personnel to lead quality management and digital transformation initiatives can cause long-term failures. P(6) added that ineffective leaders and employees fail to drive the necessary organizational changes for Q4.0, resulting in failed initiatives. Additionally, P(11) emphasized that managing Q4.0 requires effective communication of quality goals and strategies across all organizational levels, a skill often lacking in unqualified leaders.

This section categorizes Q4.0 challenges and barriers in the Middle East into four main groups as shown in Figure 4: organizational and managerial, cultural and behavioral, financial, and human resources/talent management.

The third theme is readiness factors for Q4.0. This theme includes various codes like IT infrastructure, management style, data integrity and availability, and organizational culture and change management.

P(9) noted that the region still suffers from deficiencies in technological infrastructure, requiring further enhancement to support Q4.0 initiatives. P(8) emphasized the significance of organizational collaboration among operational excellence and IT departments to lead the Q4.0 transition, emphasizing IT's pivotal role in facilitating digital transformation, yet highlighting that the operational excellence department should be given the final say. P(7) emphasized that integrating old systems with I4.0 tools integration is crucial for establishing a cohesive digital ecosystem that fosters interdepartmental collaboration, particularly between IT and operational excellence units.

Participants highlighted that Q4.0 entails reducing hierarchy and decentralizing management. P(6) emphasized reducing bureaucracy and fostering transparency as crucial steps. P(2) added that a decentralized, open management style enhances agility and responsiveness, enabling faster, data-informed decision-making.

P(9) revealed that reducing organizational hierarchies enhances readiness for Q4.0 by facilitating open communication and exchange of ideas, thereby fostering innovation and continuous improvement. Additionally, P(5) noted that transitioning to a decentralized structure is essential for Q4.0 adoption, though it remains uncommon in current organizations.

P(1) noted that production lines generate vast amounts of data, which AI algorithms can utilize to assess product quality and improve operational processes. However, P(6) highlighted a regional deficiency in data generation and utilization, representing a significant missed opportunity. Moreover, P(13) argued that data availability alone is insufficient, stressing the need for data integrity and representativeness. P(7) asserted that accurate data reflecting operational processes and customer satisfaction can enhance insights and enable targeted interventions.

Additionally, P(9) argued that the integrity and availability of data are mandatory prerequisites for implementing advanced analytics. A reliable and continuous data stream allows for real-time monitoring and control of processes, a core practice of Q4.0.

P(4) emphasized that a proper change strategy ensures organizational alignment with Q4.0 objectives. P(8) added that change management practices are critical for aligning all levels of management, departments, and employees with Q4.0, making the adoption more feasible.

Moreover, P(1), P(6), and P(10) indicated that readiness for Q4.0 involves shifting organizational culture to embrace digitization and innovation. This is achieved by embedding innovation and learning into ingrained habits, ensuring a workforce that consistently embodies Q4.0 principles in daily activities.

Process redesign is a key readiness factor identified by P(5) and P(12). The disruptive nature of Q4.0 requires a comprehensive re-evaluation and restructuring of existing processes to facilitate the integration of advanced quality management principles.

The fourth theme is core skills for Q4.0. This section includes two primary codes: soft skills and hard skills for future quality professionals.

According to P(4), critical and analytical thinking and problem-solving skills are essential for analyzing complex data and making informed decisions that enhance performance and quality.

(P1) posited that the adoption of Q4.0 is likely to encounter multifaceted resistance, encompassing both technical and cultural challenges. P(1) emphasized that addressing these challenges necessitates applying advanced critical thinking skills. This observation underscores the complex nature of Q4.0 implementation, suggesting that successful adoption requires technological readiness and the cognitive capacity to navigate and resolve intricate socio-technical issues within the organizational context.

Furthermore, P(6) argued that Q4.0's interdisciplinary approach necessitates collaboration between quality professionals, IT specialists, data scientists, and operational experts. Therefore, effective cross-functional teamwork and communication are vital skills for Q4.0 adoption. P(10) further stressed that communication skills across different teams are also essential to managing resistance and ensuring buy-in from all stakeholders.

P(7) emphasized the importance of growing organizational trust as a fundamental competency for quality management practitioners, suggesting that trust serves as a catalyst for interdepartmental data sharing. Moreover, P(14) revealed that the ability to renew and regain trust in quality management practitioners is a critical skill that facilitates change, enhances collaboration, improves data integrity, and encourages knowledge sharing.

P(2), P(4), and P(5) all emphasized the necessity of data analytics and big data skills for Q4.0 initiatives in the Middle East. Additionally, P(6) argued that some programming knowledge in AI, including machine and deep learning, can be helpful for candidates interested in pursuing a career in modern quality management. However, P(8) suggested that while comprehensive programming skills are not required, a foundational understanding of programming principles is essential.

On the other hand, P(7) asserted that proficiency in fundamental quality management techniques, including the seven basic quality tools and Statistical Process Control (SPC), remains critically important in the current landscape (Bueno et al., 2024). The participant argued that this knowledge base equips quality professionals with the ability to select between AI-driven solutions and traditional quality control methods based on the specific requirements of the quality issue at hand.

Furthermore, P(11) and P(12) revealed that quality professionals must possess project management and risk assessment skills. They argued that these skills are fundamental in planning, executing, and overseeing complex quality initiatives. P(1) added that adopting technological advancements can sometimes be accompanied by ethical considerations; therefore, it becomes more important than ever to be familiar with industry-specific regulations and standards to ensure full compliance in the digital environment.

P(4) highlighted the importance of soft skills before and at the early stages of the adoption, as managing this period effectively can determine the project's trajectory. P(13) identified hard skills—including statistical analysis, data analytics, project management, and risk management—as vital for managing the transformation process midway. However, P(6) argued that soft skills such as critical thinking, problem-solving, and communication are equally crucial for sustaining long-term benefits. P(2), P(8), and P(14) proposed that soft skills assume more importance in maintaining the transformation while acknowledging the ongoing relevance of hard skills. Figure 5 illustrates a framework showcasing the importance of both skill sets during the Q4.0 implementation phases.

To the authors' knowledge, this study marks the first attempt to investigate Q4.0 understanding, challenges, readiness factors, and required skills within the Middle East. Furthermore, by concentrating on the Middle East, a region with unique cultural, infrastructure, and strategic dynamics, this study fills a major need in the Q4.0 literature and provides context-specific insights sometimes disregarded in Western-centric studies.

Although many participants recognized Q4.0's close connection to I4.0 technologies, some viewed it narrowly as a tech-focused initiative, reflecting only a partial understanding of its multi-dimensional nature. As Sureshchandar (2023) notes, Q4.0 integrates strategic leadership, culture, customer-centric thinking, and data governance—dimensions that extend beyond mere technological adoption. The limited grasp of Q4.0's holistic scope indicates a need for more comprehensive training, awareness-building, and self-directed learning programs.

In an examination of the benefits and drivers of Q4.0 adoption, participants from diverse industries identified cost reduction, operational enhancements, real-time monitoring via digital dashboards, and improved customer satisfaction as key motivators. These findings align with existing literature in Antony et al. (2023b). However, few participants acknowledged the potential for fostering a sustained culture of continuous improvement, revealing an overlooked opportunity for long-term organizational development. This gap suggests that while immediate operational benefits are recognized, deeper cultural shifts remain underemphasized, reinforcing the notion that technology alone cannot drive lasting organizational change (Saihi et al., 2023).

While the motives and benefits of Q4.0 can be clear, the path to Q4.0 is not without obstacles. The study groups barriers into four categories: (1) organizational and managerial, (2) cultural and behavioral, (3) financial, and (4) human resources/talent management, as shown in Figure 4. Prominent among the organizational and managerial hurdles is the absence of strategic vision and inadequate leadership commitment, challenges often compounded by rigid hierarchies and misalignment with broader goals (Sandhu and Kulik, 2019). Moreover, the study identifies the tendency to prioritize short over long-term results as another major challenge (Verma et al., 2022).

In alignment with Cheng and Groysberg (2020), this research pinpoints a risk-averse culture among many organizations across various industries in the region, resulting in major issues such as resistance to change, absence of a culture for continuous improvement, and inability to make the cultural jump for Q4.0. Li and Li (2023) suggested that such a risk-averse culture can slow down innovation and prevent technology adoption. Financial challenges, such as uncertain ROI and a lack of compelling success stories, contribute to the reluctance of some organizations, particularly manufacturing and private institutions, to invest in transformational initiatives. In contrast, many participants emphasized that governmental and service organizations are willing to invest if benefits are evident. Lastly, human resource challenges, highlighted by inadequate training curricula and a shortage of digital-quality skill sets (Buhagiar, 2023; Escobar et al., 2024), emerge as a critical impediment to Q4.0 adoption. Notably, this mirrors findings from Sony et al. (2020), who emphasize that advanced I4.0 solutions require substantial workforce re-skilling.

In line with Sony et al. (2020), the study suggests that robust technological infrastructure and the ability to integrate new and existing systems are crucial readiness factors for Q4.0. However, many participants emphasized the need for substantial infrastructural upgrades and strategic partnerships to realize advanced data analytics, IoT, and AI solutions, as suggested by Gunasekaran et al. (2019). In addition, Ghobakhloo (2018) highlighted that a balance between traditional quality management tools and new technologies is crucial for adopting Q4.0. The study further identifies adopting decentralized management styles, reducing bureaucracy, and fostering cross-departmental collaboration as pivotal enablers for Q4.0. This aligns with organizational theories that highlight the role of participative management and psychological safety in driving quality improvements (Oh, 2019). Moreover, shifting the cultural fabric toward innovation and continuous learning (Virmani et al., 2024) can speed up adaptation to new digital tools and foster knowledge sharing across various departments (Kucharska and Bedford, 2020; Tortorella et al., 2020; Lam et al., 2021), making organizations more prepared for Q4.0. Ensuring reliable, representative, and real-time data is similarly vital for Q4.0 adoption since it enables predictive analytics, performance monitoring, data-driven decision-making, and overall accountability (Mejía et al., 2022; Antony et al., 2023a). Consequently, this study suggests that Q4.0 must be viewed as an evolutionary initiative that builds on existing quality management principles.

Participants underscored a need to extend beyond conventional methods (SPC, process mapping, and auditing) to incorporate advanced data analytics, AI, project management, and risk assessment (Ranjith Kumar et al., 2022; Haridy et al., 2025). Simultaneously, essential soft skills—critical thinking, communication, problem-solving, and trust-building—remain pivotal to sustaining cross-departmental collaboration (Sousa and Rocha, 2019). This resonates with organizational learning theories that blend technical competencies with behavioral and cultural adaptability for comprehensive transformation (Kolasani, 2023).

Overall, these findings reaffirm the complex interplay between technological resources, managerial infrastructure, and cultural contexts in realizing Q4.0. They also highlight that organizations—especially those in emerging markets like the Middle East—must adopt a holistic strategy that couples leadership buy-in, advanced data analytics, robust technology platforms, and cohesive change management. By following such an integrated approach, firms can realize cost savings, data-driven decision-making, enhanced quality, and real-time performance monitoring. Aligning with Antony et al. (2023a) and Martin et al. (2023), this study suggests that Q4.0 implementations are most effective when led by quality or operational excellence professionals rather than IT-only departments, as quality practitioners better appreciate process improvement principles and interlinked value chains (Saihi et al., 2023). Moreover, the study proposes a conceptual framework, shown in Figure 6, to equip firms to navigate the complexities of digital transformation, ultimately delivering sustainable improvements in efficiency, quality, and overall competitiveness.

Q4.0 remains an emerging approach with limited scholarly focus, particularly in Middle Eastern contexts (Antony et al., 2023b). Additionally, given the region's ambitious Vision 2030 agendas, there is a pressing need for Q4.0 studies that contextualize digital quality transformation within the Middle Eastern socio-economic landscape. This study addresses that gap by offering a practical exploration of Q4.0 in the Middle East, building on Escobar et al. (2024), who noted that most digital quality management research has been conducted in developed nations. The findings enrich the literature by clarifying how regional cultural, economic, and technological factors influence Q4.0 while supporting and expanding upon the definition of Q4.0 by Antony et al. (2023a). Specifically, participant responses aligned with earlier conceptualizations yet highlighted the need to expand the current definition and incorporate culture for continuous learning, data-informed processes, and organizational leadership elements. Consequently, the expanded definition views Q4.0 as a holistic approach to quality management that integrates I4.0 technologies, quality tools and techniques, and human elements to establish a culture for continuous learning, data-informed processes, and decision-making, and achieve superior quality and operational excellence. This emphasizes that Q4.0 is not merely a technological add-on but an evolutionary approach integrating I4.0 technologies with established quality principles, ultimately fostering superior operational performance (Swarnakar et al., 2025). Moreover, the findings contribute to the dynamic capabilities theory that explains how organizations adapt, integrate, and reconfigure internal competencies to remain competitive by demonstrating how Q4.0 requires firms to develop sensing, seizing, and transforming capabilities to overcome barriers in leadership vision, workforce upskilling, and IT infrastructure readiness (Teece, 2018).

From a practical standpoint, this study provides a concrete roadmap for managers and practitioners looking to transition from traditional quality frameworks to digitally enabled environments. By identifying structural and behavioral barriers, such as limited leadership commitment, insufficient training, and cultural resistance, managers can proactively formulate strategies to address these obstacles. The study also underscores critical readiness factors, including the need for top-level support, strategic planning, and a cultural shift that values continuous learning and innovation. In addition, the research outlines the pivotal digital and interpersonal skills required across various stages of Q4.0 adoption. By using these insights to develop targeted upskilling programs and informed hiring decisions, organizations can better align their human capital with the demands of digital quality management. Additionally, the widespread willingness among participants to embrace Q4.0 underlines its feasibility and strategic potential, suggesting that firms willing to invest in leadership development, skill-building, and infrastructural upgrades stand to gain significant competitive advantages in operational excellence, stakeholder satisfaction, and long-term sustainability (Nguyen et al., 2024). Based on these discussions and findings, this study proposes a four-phase (piloting, capability building, change management, and gradual integration) roadmap for Q4.0 adoption in the Middle East, detailed in Figure 7.

This qualitative study comprehensively examines Q4.0 in the Middle East by investigating current awareness levels, core motivations, challenges, readiness factors, and essential skills for Q4.0 adoption. Although participants acknowledged the advantages of leveraging advanced data analytics and AI for Q4.0, the findings reveal a marked need for targeted education, strategic vision, and robust organizational support. Key barriers include resistance to change, inadequate leadership commitment, unclear financial returns, and a shortage of training programs tailored to digital quality management. Nevertheless, strong top management support, strategic alignment, and well-designed skill-development initiatives emerged as critical readiness enablers.

Several limitations warrant caution in interpreting these results. Although 14 in-depth interviews are appropriate for qualitative research (Creswell and Creswell, 2017), a bigger sample with participants from other industries like finance and healthcare can provide more diverse perspectives across different sectors and organizational sizes. Moreover, qualitative research may introduce potential participant bias despite the standardized protocol. Furthermore, this research does not comprehensively represent the viewpoints of customers, suppliers, or regulatory bodies, whose perspectives could offer a more holistic understanding of Q4.0 ecosystems. The study's Middle Eastern focus potentially restricts generalizability to other regions, suggesting that future comparative research could expand the findings. Finally, the rapid pace of I4.0 technological advances means that certain insights may become outdated, calling for ongoing investigation. Despite these constraints, this study substantially contributes to the Q4.0 literature by addressing a notable gap in the literature and offering a foundation for future empirical and quantitative research in the region. Its insights can guide regional organizations in successfully navigating the digital transformation of quality management and, in turn, inform a broader spectrum of firms operating in similar contexts. While this study focuses on the Middle East, its findings may inspire further research in regions with similar dynamics or challenges. Future studies could employ longitudinal or mixed-methods designs to assess how Q4.0 implementation unfolds over time or engage multiple stakeholders, such as customers, suppliers, and regulatory bodies, to provide a more holistic ecosystem perspective. By broadening comparative analyses, researchers can refine the conceptual framework, thereby enhancing theoretical robustness and practical utility for diverse organizational contexts.