Adopting quality management practices in the industry 4.0 era: an investigation into the challenges

In this transformative phase of Industry 4.0, the integration of advanced digital technologies into traditional manufacturing processes presents a paradigm shift in quality management. This seminal study ventures into the forefront of this transition, uncovering the intricate challenges impeding the adoption of Quality Management Practices (QMP) amidst digital innovation. Leveraging a comprehensive survey with 172 quality management professionals, alongside the methodological rigour of Exploratory Factor Analysis (EFA) and the Analytical Hierarchy Process (AHP), our research delineates fifteen pivotal barriers to the harmonious integration of digital technologies with quality management systems. Predominantly, ‘Organisational Behaviour’ and ‘Information Technology and Governance’ surface as critical dimensions, outlining the complex landscape stakeholders must navigate. Central to our findings are leadership, cybersecurity, data protection, and organisational structure, underscored as the primary challenges that demand strategic focus. This investigation offers a pioneering framework for global stakeholders, illuminating a path forward in the quest for Quality 4.0. It marks a significant contribution to the literature by proposing a roadmap for organisations striving for excellence and innovation in the Industry 4.0 era, advocating for a seamless fusion of digital transformation and quality management.


Introduction
In the dynamic business landscape, the digital era is a transformative period marked by the comprehensive integration of digital technology in all life spheres.The Industry 4.0 era, distinguished by rapid technological evolution, sees digital tools and platforms becoming fundamental in personal and professional realms (Chawla & Goyal, 2022).Digitalisation, defined as converting information into digital format and using digital technologies to improve business operations, impacts multifaceted organisational aspects, from internal processes to customer interactions and market strategies (Ritter & Pedersen, 2020).Digital technologies are crucial in reconfiguring business architectures, necessitating a rethinking of business models and value delivery mechanisms (Bresciani et al., 2021).The digital transition, leveraging data analytics, cloud computing, and mobile technologies, opens new opportunities for organisational efficiency, innovation in products and services, and better customer need fulfilment (Wamba & Queiroz, 2022).
Industry 4.0, often termed the fourth industrial revolution, plays a significant role in digitalisation, especially in the manufacturing industry (Ghobakhloo, 2020a).Industry 4.0 integrates digital technologies into manufacturing processes, leading to 'smart factories' where cyber-physical systems monitor, create virtual replicas, and make decentralised decisions (Ghobakhloo, 2020b).Technologies like the Internet of Things (IoT), Artificial Intelligence (AI), robotics, and Big Data analytics drive increased automation, communication, and the creation of smart machines (Bai et al., 2020).
On the other hand, quality management in organisational contexts is crucial, enhancing operational efficiency, customer satisfaction, and competitive advantage (Dahlgaard et al., 2008).Deming and Juran highlight the integration of quality into organisational processes and its strategic importance in aligning business objectives with customer needs (Dahlgaard et al., 2008).However, the widespread adoption of digital technologies in the Industry 4.0 era has had profound implications for organisations and their quality management practices (Antony et al., 2022).In the Industry 4.0 era, the role of quality management has become more complex and significant (Sony et al., 2020).The rapid pace of technological advancements has introduced new dimensions to quality management, making it evolve into the so-called Quality 4.0.Souza et al. (2022) suggest that digital technologies require an adaptive quality management approach to address digital transformation challenges.Digitalisation demands agile, proactive quality management approaches, enabling real-time monitoring, predictive maintenance, and sophisticated quality control mechanisms (Maganga & Taifa, 2023).
Extensive academic research has highlighted the challenges of Quality 4.0 in organisational culture, business continuity, innovation, data quality, and cybersecurity (Dias et al., 2022;Sony et al., 2021;Souza et al., 2022).Furthermore, research has also focused on geographical and sector-specific explorations of quality management practices in the Industry 4.0 era (Antony, Sony, et al., 2023;Maganga & Taifa, 2023).Despite extensive research, a gap persists in comprehensively understanding the barriers to the effective adoption of quality management practices in this digitally transformed landscape.Our research seeks to elucidate these barriers, contributing to both academic knowledge and practical application in navigating quality management practices in the digitalised world.
The primary goal of this research is to systematically identify, categorise, and assess the barriers to adopting Quality Management Practices in the Industry 4.0 era.Through this investigation, we aim to: . To provide a detailed analysis of the main challenges faced by organisations in adopting QMP amidst digital transformation. .To evaluate the relative significance of these identified challenges and provide a comparative analysis that aids in prioritising organisational responses into their impact on QMP within the Industry 4.0 context. .To explore the implications of these challenges for the future of QMP, guiding strategic decision-making for organisations transitioning to Quality 4.0.
By achieving these objectives, this research aims to illuminate the main obstacles to QMP adoption in Industry 4.0, evaluate their comparative significance and offer actionable insights for organisations striving to align their quality management systems with the demands of the digital era.This research addresses the subsequent research questions: Total Quality Management & Business Excellence 1099 RQ1: What are the main challenges in adopting Quality Management Practices in the Industry 4.0 era?RQ2: How do the primary challenges to Quality Management Practices compare in their impact on organisations within the Industry 4.0 framework?RQ3: What are the potential implications of these challenges for the evolution and implementation of Quality Management Practices in the digital era?
This research contributes significantly to the existing knowledge base in the field of Quality 4.0 as it establishes and ranks the barriers to adopting quality practices when organisations have digitalised their operations, hence providing an understanding of the various challenges involved while also filling a gap in the academic literature.From a practical standpoint, this research contributes by providing organisations with a better comprehension of what challenges they may face when concurrently deploying quality management practices and implementing digital technologies.Ultimately, this research aspires to equip businesses with the knowledge to formulate and deploy more effective strategies to overcome these barriers and harness the full potential of digitalised quality management systems, thereby enhancing their competitiveness in the Industry 4.0 landscape.The paper is structured as follows: Section 2 offers a literature review on QMP challenges in the Industry 4.0 era.Section 3 describes the adopted research methodology.Section 4 presents the findings from the analyses.Section 5 delves into an in-depth discussion of the results, while the paper culminates with the conclusions presented in Section 6.

Literature review
Quality Management (QM) encompasses established standards, excellence models, and varied approaches that are pivotal for organisational success.In particular, Quality Management Practices (QMP) refer to procedures that ensure that products or services meet excellence standards (Flynn et al., 1995).QMP are founded on customer focus, leadership, employee involvement, a process and systematic approach to management, continuous improvement, factual decision-making, and beneficial supplier relationships (Tarí et al., 2007).
In the Industry 4.0 era, QM and QMP have evolved by integrating advanced technologies and adopting a proactive approach.Industry 4.0, marked by integrating digital technology into industrial practices, has profoundly impacted QM evolution, aiming to enhance both efficiency and quality in production through digital transformation (Gunasekaran et al., 2019).Industry 4.0 signifies a major industrial shift, introducing automation and data exchange through IoT, cloud computing, AI, and cyber-physical systems (Stentoft et al., 2021).This revolution has led to interconnected smart factories for more efficient, adaptable production processes (Bai et al., 2020).However, digital transformation in QM entails more than just adopting new technologies; it represents a paradigm shift in quality perception and management (Miloševićet al., 2022).From this paradigm shift, the concept of Quality 4.0 was born.
Quality 4.0 applies digital technologies to quality management (Sader et al., 2022).Quality 4.0 leverages data from Industry 4.0's smart systems, enhancing quality control through real-time data collection and analysis (Saihi et al., 2023).Quality 4.0 enables organisations to predict and prevent defects, optimise production quality, and rapidly respond to quality issues (Antony et al., 2022).For example, AI and machine learning in Quality 4.0 provide advanced data analytics for predictive quality and maintenance strategies.Quality 4.0 technologies can help analyse historical and real-time data, identify potential issues in advance, reduce waste, and improve product quality (Chiarini & Kumar, 2022).
Quality 4.0 transitions from conventional quality control to a dynamic, data-driven approach using IoT, AI, big data analytics, and cloud computing (Maganga & Taifa, 2023).This shift enables immediate feedback, proactive quality control, and smarter decision-making, enhancing the detection and addressing of quality issues, improving efficiency, and reducing waste (Thekkoote, 2022).However, adopting QM practices in the digital world, and hence Quality 4.0, poses significant and unique challenges for organisations (Sader et al., 2022).

Challenges for the adoption of quality management practices in the industry 4.0 era
Integrating digital tools into traditional QMSs rooted in pre-digital practices requires overcoming alignment difficulties (Antony, Sony, et al., 2023).Senna et al. (2022) highlight the challenges in retrofitting legacy systems with modern solutions like AI and big data analytics, creating a gap between technology and implementation.
The swift evolution of digital technologies demands that quality management practices adapt at a comparable pace.This adaptation necessitates significant investment in training for AI, data analytics, and IoT expertise (Antony, Sony, et al., 2023).Sader et al. (2022) highlight the need for continual learning and adaptation in quality management, which can strain resources and disrupt workflows.Digital transformation also involves investment in technology and organisational change management (Ciarli et al., 2021;Miloševicé t al., 2022).On the other hand, data management and analysis in the digital age are complex (Wamba & Queiroz, 2022).Souza et al. (2022) emphasise that managing the vast amount of data generated by digital tools requires advanced analytical skills to extract meaningful insights for quality improvement.Cybersecurity risks also heighten with reliance on digital tools in quality management systems (Escobar et al., 2021).Protecting sensitive data against cyber threats is crucial for maintaining data integrity and confidentiality.Furthermore, a cultural shift is necessary to integrate digital tools into QM, requiring a change in mindset across all organisational levels (Antony, Sony, et al., 2023).Resistance to change, lack of digital literacy, and apprehension towards new technologies can hinder the adoption of digital practices in QM (Sader et al., 2022).The digital divide poses challenges, especially for smaller organisations or those in developing regions (Maganga & Taifa, 2023).Disparities in digital technology access and expertise can lead to uneven adoption of advanced quality management practices, creating competitive disadvantages (Chiarini & Kumar, 2022).Similarly, applying QM in supply chains in the Industry 4.0 era is complex.Ranjith Kumar et al. (2022) discuss how big data analytics and IoT enhance supply chain quality management through accurate tracking and predictive analyses.However, digital transformation within supply chains also demands new competencies and adaptation to changing environments (Cubo et al., 2023).
Integrating Quality 4.0 within Industry 4.0 environments involves challenges such as ensuring data integration and interoperability among diverse digital tools and platforms (Ali & Johl, 2022;Souza et al., 2022).Moreover, the shift to Quality 4.0 requires a workforce skilled in digital technologies and data analytics, necessitating ongoing training and development (Miloševićet al., 2022;Saihi et al., 2023).All these are considered some of the major barriers that organisations will need to overcome to implement and combine QMP and digital technologies effectively.
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Research methodology
This study started by reviewing contemporary literature to decipher the challenges for the adoption of QMP in the Industry 4.0 era.A comprehensive and iterative literature search was employed using the Scopus database.The collated literature underwent rigorous evaluation, initiating with a preliminary abstract analysis and culminating in an in-depth perusal of pertinent full-text articles.This thorough methodology, underpinned by academic exactitude, facilitated a holistic definition and comprehension of the barriers, blending scholarly and pragmatic vantage points.Table 1 presents the barriers discerned from the literature.
As shown in Figure 1, the research methodology followed a tripartite structure to probe QMP adoption challenges in the Industry 4.0 era.An extensive literature review initially yielded 15 potential obstacles, see Table 1.The subsequent phase involved an empirical inquiry through a quantitative survey, collating insights from a total of 172 industry professionals.This data underwent an Exploratory Factor Analysis (EFA) for validating and categorising the challenges.The culminating stage adopted the Analytical Hierarchy Process (AHP) technique for hierarchically ranking the QMP adoption challenges, emphasising their contextual relevance in the digital era.The following section details this methodology, accentuating its potential for replication in subsequent similar investigations.

Data collection and analysis
The questionnaire comprised two segments, i.e. demographics and a detailed exploration of the 15 challenges related to QMP adoption in the Industry 4.0 era.The initial part of the questionnaire aimed to gather demographic details, focusing on the respondents' industry background, organisation size and region.This approach enabled nuanced analysis by accounting for variances among respondents.In the primary research section, we asked participants to assess 15 impediments associated with QMP adaptation in the Industry 4.0 era.We used a Likert scale anchored at 1 (Strongly Disagree) and culminating at 5 (Strongly Agree).Table 2 outlines both sections in detail.Industry and scholarly experts validated the questionnaire to confirm its robustness, and it underwent beta testing to ensure clarity and consistency before its broad-scale deployment.
The survey was disseminated through email and LinkedIn, capitalising on a diverse network of scholars and practitioners.This approach conformed to the suggested 5:1 ratio of respondents to variables for EFA (Reio & Shuck, 2015), for which the 176 responses collected were acceptable to conduct the EFA.Post-collection data underwent stringent cleaning, coding, and validation in SPSS.Demographic analyses and EFA proceeded using SPSS to structure the barriers into coherent clusters.The AHP then facilitated the structured ranking of the identified challenges.The methodology followed in this study is consistent with the methodological approach outlined in previous literature (Luthra & Mangla, 2018).

Demographic analysis
In examining respondent demographics, this study methodically evaluated three salient criteria, i.e. industry type, organisational size, and geographical location.This selection was grounded in expert recommendations for survey face validity and paralleled methodologies documented in prior research (Luthra & Mangla, 2018).Table 3 delineates this demographic distribution, showcasing frequency analyses of variables (DM1 to DM3, expounded in Section 4.2) from 172 valid responses, ensuring methodological rigour and credibility of derived insights.The challenge highlights insufficient engagement from top-level executives in adopting new digital quality management practices.This challenge leads to struggles in modernising and optimising quality systems essential in today's fast-evolving digital environment.(Ali & Johl, 2022;Antony, McDermott, et al., 2023;Gunasekaran et al., 2019;Miloševićet al., 2022;Sony et al., 2021) 2

Poor organisational structure
The challenge refers to a disjointed setup that hampers the seamless integration of digital quality management practices, hindering efficient team communication and coordination.This structural deficiency can impede the successful adaptation to the Industry 4.0 era's demands for streamlined quality management.(Antony, McDermott, et al., 2023;Sony et al., 2020;Souza et al., 2022) 3

Financial issue
The challenge entails constraints in budget allocation for implementing digital quality management practices, potentially limiting investments in technology and resources necessary for optimal adaptation to the Industry 4.0 era's quality requirements.This financial challenge can hinder the organisation's ability to stay competitive and compliant in the digital landscape.(Antony, McDermott, et al., 2023;Antony, Sony, et al., 2023) 4

Lack of competencies
The challenge denotes a shortage of skills and expertise needed to effectively implement and manage digital quality practices, potentially leading to inefficiencies and quality gaps in the organisation's digital transformation efforts.This competency gap can impede the successful adaptation to the demands of the Industry 4.0 era.(Ali & Johl, 2022;Kannan & Garad, 2020;Miloševicé t al., 2022;Zonnenshain & Kenett, 2020) 5

Resistance to change
The challenge signifies the reluctance of employees to embrace new digital quality management methods, hindering the smooth transition and adoption of innovative practices crucial for staying competitive in the Industry 4.0 era.(Antony, McDermott, et al., 2023;Virmani et al., 2023) 6

Leadership
The challenge emphasises the critical role of strong and visionary leadership in driving the adoption of digital quality management practices.Effective leadership is essential for guiding the (Miloševićet al., 2022;Rico et al., 2024;Sader et al., 2022;Sony et al., 2020;Stentoft et al., 2021;Stentoft & Rajkumar, 2020) (Continued) Data integration and data management The challenge highlights the difficulties in seamlessly merging and effectively managing data from various digital sources, impacting the organisation's ability to derive valuable insights and maintain data quality.(Ali & Johl, 2022;Antony, Sony, et al., 2023;Ranjith Kumar et al., 2022;Saihi et al., 2023) 8 Regulatory approval and government support The challenge involves navigating complex regulatory frameworks and securing government backing for digital quality management initiatives, which can be time-consuming and challenging but is essential for compliance and success in the Industry 4.0 era.(Maganga & Taifa, 2022;Sony et al., 2021) 9 System-thinking approach The challenge underscores the need to shift from siloed to holistic thinking in quality management, recognising the interdependencies of digital processes.Embracing this approach is crucial for effective adaptation to the Industry 4.0 era, ensuring seamless quality across interconnected systems.(Antony, Sony, et al., 2022;Virmani et al., 2023) 10 Innovation ecosystem The challenge highlights the necessity of fostering a conducive environment for continuous innovation in quality management practices involving collaboration, experimentation, and technology adoption.Nurturing this ecosystem is vital for staying competitive and adaptive in the digital era.(Antony, Sony, et al., 2023;Miloševićet al., 2022;Souza et al., 2022) 11 Learning organisation The challenge emphasises the importance of cultivating a culture of continuous learning and adaptation to new quality management methodologies and digital tools.Becoming a learning organisation is key to thriving in the ever-evolving digital landscape.(Antony, Sony, et al., 2023;Miloševićet al., 2022;Rico et al., 2024;Stentoft & Rajkumar, 2020) 12 Cyber security and data protection The challenge involves safeguarding digital quality management systems and sensitive data from cyber threats, requiring robust security measures and compliance with data protection regulations to maintain trust and integrity in the Industry 4.0 era.(Sader et al., 2022;Saihi et al., 2023;Sony et al., 2021) 13 Proper technologies The challenge underscores the need to select and implement the right digital tools and technologies for quality management, ensuring they align with organisational goals and processes to optimise performance in the digital era.Making informed technology choices is crucial for successful adaptation.14 People's involvement The challenge emphasises the importance of engaging and motivating employees to actively adopt digital quality management practices, as their buy-in and collaboration are essential for successful adaptation and quality improvement in the Industry 4.0 era.(Gunasekaran et al., 2019;Martin et al., 2021;Rico et al., 2024) 15 Internet speed The challenge relates to the reliance on fast and stable internet connections for efficient implementation of digital quality management practices, as slow or unreliable internet can hinder data transfer, communication, and access to digital tools, impacting overall quality processes in the Industry 4.0 era.(Antony, Kaul, et al., 2024;Maganga & Taifa, 2022) Total Quality Management & Business Excellence 1105

Data cleansing and coding
Rigorous data cleaning and coding procedures were employed to maintain data integrity during the analysis.Out of the initially received 193 responses, 172 were considered suitable for further examination after a stringent review for data completeness and accuracy.The number of participants exceeded the projected sample size of 125 needed to conduct a valid EFA (Reio & Shuck, 2015).Notably, the attained sample size is comparable to, and even exceeds, those observed in other studies that have employed EFA (Maskey et al., 2018).We denoted demographic questions as 'DMx', see Table 3, and tagged questions concerning challenges as 'Cx', see Table 4.We quantified participant responses using a Likert Scale that ranged from 1, indicating 'Strongly Disagree,' to 5, representing 'Strongly Agree'.

Assessment of reliability and validity
Responses were instantaneously captured via a survey platform and securely stored to ensure the reliability of the collected data.The dataset, identified by variable codes C1 through C15, underwent a reliability assessment using Cronbach's Alpha, achieving a value of 0.836.This result surpasses the commonly accepted benchmark of 0.7, confirming the internal consistency and reliability of our survey instrument (Taber, 2018).Table 4 presents the findings.Our crosssectional study relied solely on internal consistency for reliability since participants could respond only once, making other measures like stability impractical.The validity of our survey was established through several measures.Initially, content validity was ensured by aligning the survey content with existing scholarly literature.A pilot test was conducted to confirm face validity.Three academic experts with more than 15 years of experience and three quality management experts with more than 10 years of experience participated in the pilot testing.
Crucially, discriminant validity was assessed to ensure that each barrier measured a distinct construct.This assessment was conducted by applying the Pearson Correlation Coefficient to the barriers rated by respondents.The correlation matrix, as shown in Table 5, revealed that all observed variables exhibited weak to moderate correlations, with none exceeding an absolute value of 0.6.This threshold was carefully chosen based on established methodological standards, including those recommended by Chien et al. (2011), which suggest that correlation coefficients below this value adequately demonstrate that the constructs being measured are indeed distinct and not overlapping.The significance of these findings cannot be overstated, as they validate the discriminant validity of our identified barriers, ensuring that each one distinctly contributes to our understanding of the challenges involved in adopting QMP in the Industry 4.0 era.The careful consideration of these correlation values and the adherence to methodological guidelines underscore the rigour with which the study's constructs were evaluated and confirmed.Furthermore, this approach not only aligns with the methodological approach outlined in the previous literature but also significantly contributes to the needs of this paper by establishing a clear, empirical foundation upon which the distinctness of each measured barrier rests.
Table 4 showcases the results of the descriptive analysis, where mean values exceeded the threshold of 2.5, underlining the significance of the barriers and thereby justifying their inclusion in the EFA (Taber, 2018).(Chien et al., 2011).The study employed Cronbach's Alpha as a confirmatory measure to affirm the reliability of the identified variables, thereby supporting the decision to advance with EFA, see Table 6.A Kaiser-Meyer-Olkin (KMO) value of 0.837 corroborated the data's suitability for EFA, exceeding the recommended threshold of 0.7 (Eisinga et al., 2013).Simultaneously, Bartlett's Test of Sphericity yielded a significance level of 0.001, considerably below the accepted 0.05 cut-off (Shrestha, 2021).These results further substantiated the data's appropriateness for conducting EFA to explore the challenges of adopting QMP in the digital era.
The subsequent phase entailed discerning factors within the EFA representing the cumulative variance linked to the challenge variables (C1 to C15).In a principal component analysis, three variables with Eigenvalues exceeding the threshold level of 1 were identified, representing 50.69% of the total variance (Luthra & Mangla, 2018).While this does not meet the Yong and Pearce (2013) criterion of 75% variance explained, it is consistent with the 50% average variance observed in studies utilising EFA (Henson & Roberts, 2006).It also satisfies the commonly accepted variance coverage of above 50%, as suggested by Williams et al. (2010).A Varimax rotation subsequently clarified factor loadings (Osborne, 2019).The QMP challenges were then mapped to three factors, omitting loadings below 0.30 after iterations.Table 7 illustrates the structure of the rotated component matrix, which was utilised to create a new model of the EFA factors.
Furthermore, these three components resulting from the EFA were used to attain the barrier classification into meaningful and comprehensive categories, as illustrated in Table 8.Cross-loading concerns were minimal, adhering to Zhang et al. (2018).The evolved structure, rooted in Table 7's rotation matrix, effectuated a cogent classification of the challenges, meeting RQ2 objectives.The dominant variable values in the matrix satisfied Samuels's (2017) 0.4 threshold, and the 50% variance conformed to standards by Baig et al. (2020) and Williams et al. (2010).Ultimately, industry consultation validated these categories, with Table 8 detailing the finalised challenges within the three categories.

Hierarchical prioritisation of QMP challenges
The following stage in this study consisted of prioritising/ranking the challenges to QMP adoption in the Industry 4.0 era.To do this and given its effectiveness, AHP was employed.
The AHP provides a structured framework for dissecting complex decisions, incorporating essential tiers like objectives, criteria, and sub-criteria (Luthra & Mangla, 2018).Although alternatives like ANP, ELECTRE, and TOPSIS exist, AHP's intuitiveness makes it preferable for comparative dilemmas (Luthra et al., 2017).Applied broadly in sectors such as manufacturing and logistics (Ho & Ma, 2018), AHP's unique hierarchical approach notably simplifies intricate tasks (Dos Santos et al., 2019)..001(<)Cronbach's Alpha (No of items = 15) 0.836 Luthra et al. (2016) indicate that AHP does not mandate a large sample, but the quality of expert input is vital.Consistent with prior studies, this research engaged seventeen QM experts (Luthra et al., 2017).The participants were professionals with substantial expertise in quality management implementation projects within various industrial domains.Each Total Quality Management & Business Excellence 1111 possessed a minimum of a decade of professional engagement in the quality management domain.Their professional roles encompassed diverse positions, indicative of their breadth of experience and leadership within the industry.Table 9 profiles these experts.The AHP aimed to determine global weights to rank the QMP adoption challenges in the Industry 4.0 era.The results led to a three-tier hierarchy, illustrated in Figure 2, that evaluated the challenges by their degree of importance.The three-tier order includes the primary goal (Level I), dimensions from EFA (Level II), and the 15 specific challenges (Level III).Seventeen experts compared the challenges using Saaty's nine-point scale (Saaty, 1987).Following the pairwise comparisons, relative priority weights emerged from aggregated expert insights, see Table 10.While both mean and median are potential aggregation methods, this study selected the median due to its statistical robustness (Pauer et al., 2016), indicating the mean's vulnerability to outliers and reduced robustness, respectively.
The consistency ratio (CR) for all matrices was then calculated to ensure reliability in ranking the barriers.During data acquisition, specific experts adjusted responses to achieve an acceptable CR (≤ 0.2), as recommended by Pauer et al. (2016).This refinement facilitated the final matrix aggregation.The AHP analysis ranked 'Organisation Behaviour (OB) (0.5816)' as the highest, followed by 'Information Technology and Governance (ITG) (0.3090)' and 'Ecosystem Enablers (EE) (0.1095)'.Detailed priority weights are presented in Table 11.Based on the global importance of the challenges, 'Leadership (OB3) (0.4610)', 'Cyber security and data protection (ITG1) (0.1373)', 'Poor organisational structure (ITG2) (0.0824)', 'Resistance to change (OB2) (0.0763)', and 'Data integration and data management (ITG5) (0.0496)' had the highest global weights and can hence be considered as the top 5 challenges that companies need to overcome to adopt QMP in a digitalised environment.

Discussion
This study systematically identified and investigated 15 critical challenges to adopting Quality Management Practices (QMP) in the Industry 4.0 era, as delineated by the insights of 172 industry experts and substantiated through the rigorous application of Exploratory Factor Analysis (EFA).These challenges, organised into three distinct dimensions ('EE-ITG-OB') -' Ecosystem Enablers (EE)', 'Information Technology and Governance (ITG)', and 'Organizational Behavior (OB)'-were methodically ranked using the Analytic Hierarchy Process (AHP), see Table 11.This structured prioritisation not only reveals the multifaceted nature of modern quality management challenges but also provides a strategic framework for effectively addressing them.Our discussion delves into the implications of these findings, highlighting the paramount importance of leadership in navigating the complexities of digital transformation, the critical need for enhanced By situating these challenges within the broader context of Industry 4.0, this study illuminates the intricate interplay between technological advancements and organisational dynamics, offering actionable insights for practitioners and policymakers alike.It underscores the imperative for a holistic approach that integrates technological, strategic, and human factors in overcoming the barriers to effective QMP adoption in this new era.Moreover, this analysis serves as a foundation for future investigations, suggesting avenues for deeper exploration of the nuanced interactions between these challenges and their cumulative impact on quality management practices in a rapidly evolving digital landscape.The investigation into the adaptation of QMP in the Industry 4.0 era decisively points to 'Organizational Behavior (OB)' as a pivotal dimension.This study not only corroborates but also extends the findings of Sony et al. (2020), who underscore the indispensable need for an organisational culture finely tuned to the demands of Quality 4.0.The importance of OB, as highlighted in our research, encompasses the spectrum of interactions and collaborative efforts required among individuals and teams to respond to the rapid technological shifts characterising this era effectively.Moreover, Marion and Fixson (2021) emphasise the criticality of OB in ensuring that such technological evolutions do not disrupt but rather enhance workflows and communication systems within organisations.This recognition of OB's centrality affirms its crucial role in facilitating organisational adaptability and resilience in the face of digital transformation challenges.
'Leadership (OB3)' emerges as a preeminent challenge with the highest global ranking, aligning with the insights provided by Sony et al. (2020).This study's findings delineate the indispensable role of leadership in shepherding organisations through the tumultuous phase of digital transformation.The role of leadership encompasses not merely the setting of strategic direction for quality management but extends to ensuring the alignment of quality initiatives with the broader organisational goals; a perspective also echoed by Antony, Sony, et al. (2023) and Maganga and Taifa (2022).The multifaceted challenges of technology adoption, resource management, and process redesign are underscored by Fonseca et al. (2021) as areas where effective leadership is critically needed.Our findings advocate for a comprehensive approach whereby leadership development, organisational cultural change, and strategic alignment are seen as interlinked strategies essential for navigating the complexities of digital adoption.Such an approach enhances the efficacy of quality management practices, offering a roadmap for organisations striving to excel in the Industry 4.0 landscape.
The challenge of 'Cyber Security and Data Protection (ITG1)' has emerged as the second most critical concern, underscored by the advent of cloud technology, IoT, and big data, which significantly increase the exposure to cyber risks (Awaysheh et al., 2022).This situation necessitates a robust framework for data integrity and defence against cyber threats, which is essential for maintaining quality standards compliance (Dias et al., 2022).The evolving digital landscape demands that organisations not only recognise these risks but also actively implement measures to mitigate them, ensuring the protection of sensitive data and the continuity of quality management practices in the face of ever-present cyber threats.
'Poor Organisational Structure (ITG2)' is identified as the third significant barrier in the global ranking for the effective integration of quality management practices in the context of digital transformation (Zeng et al., 2017).The prevalence of organisational silos complicates essential cross-functional collaboration (Broday, 2022), impeding the seamless streamlining of processes necessary for digital adaptation.Addressing this challenge involves a deliberate shift towards agility-focused organisational restructuring.This shift encourages interdisciplinary teamwork and aligns digital roles within the organisation, thereby facilitating a smoother transition to digital operations and enhancing the organisation's readiness for embracing digital change.
Furthermore, 'Resistance to Change (OB2)' and 'Lack of Management Support (OB1)' highlight the complexities of human dynamics in organisational transformation.The reluctance of the workforce to adopt new practices, driven by a preference for familiar routines, poses a substantial obstacle to the adoption of advanced quality management technologies (Antony, Sony, et al., 2023).This resistance is compounded by a lack of supportive leadership, which is critical for providing clear direction, resources, and Total Quality Management & Business Excellence 1115 communication to foster an environment conducive to change.Addressing the Lack of Competencies (ITG3) necessitates a focused approach to employee empowerment, emphasising the benefits of change and providing targeted training to bridge knowledge gaps.This strategy, supported by proactive leadership and a firm commitment to quality-driven digital adaptation, is essential for navigating the challenges of digital transformation successfully.
In the digital era, 'Integration and Data Management (ITG5)' emerges as a significant challenge, ranking fifth among 15 barriers, highlighting its crucial role in quality management efforts.The fragmentation of data across diverse digital tools complicates decision-making, underscoring the necessity for seamless data integration.This challenge is further underscored by the 'System Thinking Approach (EE5)' positioned at the eleventh place, which illuminates the critical interdependencies within organisational systems, necessitating a holistic view facilitated by integrated data.Achieving this level of integration mandates the eighth-ranked barrierthe adoption of 'Proper Technologies (EE2)', such as AI, IoT, big data, and cloud computing, which are essential for effective data collection, storage, and analysis (Sony et al., 2020).The absence of such technologies leads to inadequate data integration, thereby diminishing the efficacy of quality management practices (Bousdekis et al., 2023).This tiered analysis reveals a clear pathway for enhancing QMP by addressing these ranked challenges, with a particular focus on the interaction between technology adoption and organisational strategy to overcome data integration hurdles (Christou et al., 2022).
In the realm of Ecosystem Enablers (EE) dimensions affecting quality management in the Industry 4.0 era, 'People Involvement (EE2)' is identified as a crucial factor, securing the eighth position in the global ranking out of 15 identified barriers.These findings highlight its significant role in facilitating the seamless adoption of quality practices, reducing resistance, and boosting adoption rates (Sader et al., 2022).Establishing a continuous learning culture and actively involving employees in enhancing their skills are pivotal for increasing organisational flexibility.
Ranked 13th, the 'Learning Organization (EE4)' complements 'People Involvement (EE2)' by not just engaging employees but also preparing them for the nuances of digital transformation, demonstrating the lower yet vital significance of fostering an environment where learning and adaptability are prioritised (Swarnakar et al., 2023).Positioned 12th, the 'Innovation Ecosystem (EE1)' underscores the importance of nurturing a setting where innovative technologies and methods are leveraged to address quality management challenges creatively.This ranking reflects its perceived impact relative to other barriers, emphasising the value of promoting an innovation-driven culture to navigate the complexities of quality management in the digital age (Benitez et al., 2020).This structured analysis based on the global AHP ranking illuminates the criticality of each barrier within the Ecosystem Enablers (EE) dimension, guiding organisations on where to focus their strategic efforts to enhance quality management practices effectively.By addressing these ranked challenges, organisations can foster a more innovative, adaptable, and engaged workforce prepared to meet the demands of Industry 4.0.
In our analysis, 'Regulatory Approval and Government Support (ITG4)' emerges as a notable external factor from the Information Technology and Governance (ITG) dimension, ranking 10th out of 15 in influencing the adoption of digital transformations.This barrier underscores how the rapid pace of digital innovation can outstrip existing regulatory frameworks, posing challenges for aligning quality management practices with new technological advancements (Al-Emran and Griffy-Brown, 2023).Government incentives are highlighted as beneficial for easing this transition, yet the necessity for proactive engagement with regulatory authorities and flexibility in regulatory compliance is emphasised to navigate the digital landscape adeptly.
Additionally, positioned 14th in the global ranking and falling under the Ecosystem Enablers (EE) dimension, 'Financial Issues (EE7)' specifically addresses the economic hurdles faced predominantly by Small and Medium-sized Enterprises (SMEs) in adopting digital technologies.This barrier accentuates the need for meticulous financial planning and cost-benefit analysis to make digital transitions financially sustainable (Masood and Sonntag, 2020).By delineating the rank and dimension of these barriers, the study draws attention to the distinct yet intertwined challenges organisations face in the digital era.Regulatory Approval and Government Support (ITG4) is identified as a significant but not insurmountable obstacle, while Financial Issues (EE7), though ranked lower, still represent a critical area for strategic financial management and planning, especially for SMEs navigating the complexities of digital adoption.
Placed at the 15th position in the AHP global ranking, 'Internet Speed (EE6)' emerges as the least ranked yet still crucial barrier within the Ecosystem Enablers (EE) dimension, highlighting its foundational importance in the era of digital transformation.Slow internet speeds present significant challenges to efficient data sharing and the effective use of cloud-based applications, a hurdle particularly pronounced in developing regions lacking access to reliable, high-speed internet connections (Maganga & Taifa, 2022).Addressing this issue calls for a concerted effort to improve Internet infrastructure, necessitating collaborative initiatives among government bodies, the private sector, and international organisations to ensure the widespread availability of robust Internet services.Despite its ranking, the barrier underscores a basic yet vital need for facilitating digital adoption and optimising the benefits of Industry 4.0 technologies across global communities.

Conclusions
This study systematically explores the multifaceted challenges to adopting QMP in the context of Industry 4.0.Employing Exploratory Factor Analysis (EFA) and Analytic Hierarchy Process (AHP), it identifies and categorises 15 key challenges into three distinct dimensions, offering an insightful perspective across various industries and geographical locations.This pioneering research, drawing on the expertise of 172 industry professionals, fills a critical gap in the existing literature by introducing a tailored framework for understanding and tackling these challenges effectively.
This study lays the groundwork for a deeper understanding of QMP adoption barriers in Industry 4.0, proposing a collaborative, multi-stakeholder approach for future explorations.By expanding the academic conversation and offering pragmatic solutions, this research highlights the ongoing need for adaptation and innovation in the face of Industry 4.0's challenges, paving the way for further investigations that build upon these foundational insights.
Our findings present a structured approach to navigating Industry 4.0's complexities, emphasising significant barrier dimensions: Organisational Behaviour (OB), Information Technology and Governance (ITG) and Ecosystem Enablers (EE).Among the barrier criteria, leadership (OB3), cyber security and data protection (ITG1) and poor organisational structure (ITG2) emerge as primary concerns, highlighting the need for strategic cultural shifts and technological upgrades for successful QMP implementation.
The implications of this research extend into practical applications, shaping policymaking, organisational strategies, and societal advancement.It reveals the intricate Total Quality Management & Business Excellence 1117 interplay of challenges, advocating for a unified approach to enhance systems thinking, data management, and technology adoption.
In addressing the inherent limitations of our study, it is crucial to acknowledge the constraints that shape our research findings.Our study's focus on the adoption of quality management practices within the Industry 4.0 context may encounter limitations stemming from the specificity of our data sources and expert consultations.Given the diverse regulatory environments, technological infrastructures, and cultural attitudes towards quality management practices across different regions, the generalizability of our conclusions may be impacted.
The study's methodology, particularly its reliance on convenience sampling, may limit the generalizability of its conclusions.Subsequent research should adopt more diverse sampling techniques to ensure the robustness of future findings and address this methodological issue.Moreover, the rapid progression of Industry 4.0 technologies necessitates ongoing investigation into new and emerging practices to maintain the relevance of research outcomes.
The Analytic Hierarchy Process (AHP), despite its effectiveness, introduces a degree of subjectivity through expert judgments, potentially simplifying the complex relationships among identified challenges.Future research directions include exploring alternative decision-making models like the Fuzzy AHP or Best-Worst Method (BWM) for a comprehensive analysis.Integrating network analysis with AHP could also uncover deeper connections between challenges, offering strategic insights for holistic problem-solving.
Additionally, the rapidly evolving landscape of Industry 4.0 technologies presents a dynamic backdrop against which our study's findings may quickly become outdated.As new technologies emerge and existing ones evolve, the challenges and opportunities associated with adopting quality management practices may shift, necessitating ongoing research efforts to capture these changes effectively.
Furthermore, while our investigation offers a comprehensive examination of the challenges associated with quality management practices adoption, it may not fully explore the nuances of emerging technological advancements that could reshape our understanding of these challenges.This limitation highlights the need for future research to incorporate cutting-edge technological insights into the analysis.
In light of these limitations, our study should be viewed as an initial exploration into the multifaceted challenges of adopting quality management practices in the Industry 4.0 era.We advocate for future inquiries to broaden the geographical scope, incorporate the latest technological insights, and remain adaptable to the ever-changing landscape of policy reforms and technological advancements.This approach will ensure a more comprehensive understanding of the challenges and opportunities inherent in adopting quality management practices in the dynamic context of Industry 4.0.
Further research should delve into comparative studies across different sectors and cultural contexts to ascertain the universality of identified challenges.Additionally, longitudinal studies tracking the evolution of these challenges over time could provide valuable insights into the dynamic nature of Industry 4.0 adaptation.Investigating the efficacy of specific interventions and strategies in real-world settings would also be invaluable, providing empirical evidence to guide future quality management initiatives.This line of inquiry could reveal critical insights into the adaptability and scalability of quality management practices in the global arena.
Additionally, the application of simulation-based contingency analysis to explore the interrelationships among the identified challenges offers a promising direction.Such an approach could simulate various scenarios, providing a dynamic model of how addressing one challenge might influence others.Such a comprehensive approach could lead to a better understanding of the systemic nature of these challenges and the cascading effects of potential interventions.

Figure 2 .
Figure 2. Decision hierarchy of the QMP adoption challenges.

Table 1 .
Summary of challenges for the adoption of QMP in the Industry 4.0 era.
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Table 2 .
Description of survey questions.

Table 3 .
Demographic analysis of survey data.

Table 4 .
Reliability Test Outcome of Survey Data.

Table 5 .
Correlation matrix of challenges for adapting quality management practices in the digital era.

Table 7 .
EFA framework for three distinct challenge dimensions.

Table 9 .
Quality expert panel for AHP.

Table 11 .
Ranking of challenges in adapting quality management practices in the Industry 4.0 era.