Strategic Human Resource Development for Industry 4.0 Readiness: A Sustainable Transformation Framework for Emerging Economies

Abstract

Industry 4.0 represents a significant transformation in industrial systems through digital integration, presenting both opportunities and challenges for aligning the workforce, especially in emerging economies like Thailand. This study adopts a sequential exploratory mixed-method approach to investigate how strategic human resource development (HRD) contributes to sustainable transformation, defined as the enduring alignment between workforce capabilities and technological advancement. The qualitative phase involved case studies of five Thai manufacturing firms at varying levels of Industry 4.0 adoption, utilizing semi-structured interviews with executives and HR leaders. Thematic findings informed the development of a structured survey, distributed to 144 firms. Partial Least Squares Structural Equation Modeling (PLS SEM) was used to test the hypothesized relationships among business pressures, leadership support, HRD preparedness, and technological readiness. The analysis reveals that business pressures significantly influence leadership and HRD, which in turn facilitate technological readiness. However, business pressures alone do not directly enhance readiness without the support of intermediaries. These results underscore the critical role of integrated HRD and leadership frameworks in enabling sustainable digital transformation. This study contributes to theoretical perspectives by integrating HRD, leadership, and technological readiness, offering practical guidance for firms aiming to navigate the complexities of Industry 4.0.

1. Introduction

A global digital transformation framework, such as Industry 4.0, signifies a technological shift in the manufacturing landscape, characterized by the integration of digital technologies, such as big data analytics, cyber–physical systems (CPS), the Internet of Things (IoT), and artificial intelligence (AI) [1,2]. These technologies have revolutionized manufacturing processes, enabling unprecedented efficiency, innovation, and competitive advantage across industries [3,4]. The rapid adoption of Industry 4.0 technologies presents considerable challenges, particularly for manufacturing entrepreneurs, who must integrate these technologies with a workforce that is not only technically proficient but also adaptable to the evolving demands of digital transformation [5]. Recent studies emphasize the strategic role of human resource development (HRD) in facilitating Industry 4.0 readiness and advancing sustainability objectives in emerging economies. Ref. [6] demonstrates that management support and external environmental factors significantly influence the successful adoption of HRD, thereby enhancing employee engagement and organizational performance. Similarly, ref. [2] emphasizes the need for HRD integration with smart technologies and labor upskilling to enable socio-economic sustainability. Together, these findings position HRD as a key driver of sustainable digital transformation in manufacturing sectors. To achieve sustainable development, various researchers have developed Industry 4.0 readiness and maturity models, aiming to help enterprises understand their current capabilities and identify solutions for successful digital transformation [7]. Nevertheless, the advancement of Industry 4.0 readiness assessment and maturity frameworks remains ambiguous.

Recent literature has explored various dimensions of Industry 4.0 and its implications for human resource development (HRD); however, significant gaps still need to be identified. For example, ref. [3] examined the practice patterns of Industry 4.0 technologies in production plants but focused predominantly on the technological aspects, leaving the human resource dimension underexplored. Similarly, ref. [8] discussed the sustainability opportunities of Industry 4.0 digitization, but a comprehensive framework for HRD strategies was needed to support these technological advancements. Ref. [9] investigated the role of dynamic capabilities in digital transformation; however, their focus was limited to a general regional context rather than specific contexts, such as Thailand. In addition, ref. [10] analyzed Industry 4.0 through the lens of management fashion theory, highlighting its rise but not adequately addressing the strategic HRD required to sustain this transformation. These studies highlight the need for research that bridges the gap between technological readiness and the human resource capabilities necessary to fully leverage Industry 4.0 technologies. Supported by the study of [7], existing Industry 4.0 models require additional research in organization, strategy, and human resources, particularly in leadership, soft skills, education, and training.

This paper addresses the critical need to enhance Industry 4.0 readiness through strategic human resource development (HRD), with a focus on manufacturing sectors, where digital transformation is crucial for maintaining global competitiveness. While prior research has primarily emphasized the technological and organizational dimensions of Industry 4.0, most of it has centered on developed economies with advanced digital infrastructures. In contrast, limited attention has been given to how manufacturing firms in emerging economies, such as Thailand, build Industry 4.0 readiness through HRD initiatives. Existing literature often overlooks the complex interplay of institutional, social, and capability-building factors that influence transformation in resource-constrained environments [4]. To address this gap, the present study proposes a strategic HRD framework tailored to the unique needs of manufacturing entrepreneurs in Thailand. The framework emphasizes continuous learning, skill enhancement, and a culture of innovation as key enablers of digital transformation [4,8]. Drawing on case studies from Thailand, an emerging economy with a distinctive economic, cultural, and technological context, this research offers practical insights applicable to similar markets. By integrating organizational strategy with human capital development, the study contributes to a more holistic understanding of Industry 4.0 transformation in diverse socio-economic settings.

Given the limited understanding of how manufacturing firms in emerging economies develop Industry 4.0 readiness through strategic human resource development (HRD), this study explores the mechanisms by which such firms enhance their digital transformation capabilities through HRD initiatives. The focus is placed on examining the intersection between digital transformation and human capital development within resource-constrained environments.

This investigation aims to uncover the strategic HRD practices that contribute to building organizational readiness for Industry 4.0. In this manner, the research contributes to a deeper understanding of how firms align workforce capabilities and organizational strategies to support the adoption and implementation of advanced manufacturing technologies.

The contribution of this research is twofold. First, it offers a comprehensive strategic HRD framework to enhance Industry 4.0 readiness, particularly in developing economies like Thailand. Second, it provides empirical insights through a mixed-method approach. The methods combine the elements of two types of research, including qualitative case studies with quantitative analysis. This offers a robust foundation for understanding the interplay between technological readiness and human resource capabilities. The research results provide new practical guidance on Industry 4.0 maturity stages and elaborate on the effects of leadership and workforce readiness as key dimensions for achieving a higher level of Industry 4.0. By addressing the critical gap between technological advancements and workforce preparedness, this research aims to support enterprises in maximizing the strategic value of Industry 4.0 implementation, thereby enhancing their global competitiveness. Moreover, policymakers and entrepreneurs can navigate these pivotal stages to establish enduring and impactful sustainable businesses.

2. Literature Review

2.1. Conceptual Foundations of Digital Transformation and Industry 4.0

Digital transformation has emerged as a significant phenomenon for academics and practitioners across various industries [11,12,13]. The digital transformation of enterprises encompasses the transformation of corporate strategy, business model, organizational culture, management, products, and marketing [14,15]. Before examining the progression of digital transformation within business operations and organizational management, it is essential to clarify its distinction from related concepts that are often conflated [16,17,18]. The authors of [13] conceptualize digital transformation as comprising three sequential stages: digitization, digitalization, and digital transformation. The literature widely supports the view that successful transformation is contingent upon the completion of the first two foundational stages [19,20].

Digitization refers to the conversion of analog content or manual processes into digital form [18,21,22]. It involves encoding information into binary data and often includes automation through information technologies [17,23]. Digitization also supports cost efficiency and task integration via IT-enabled resource management [24,25].

Digitalization extends beyond digitization by enabling new modes of communication, collaboration, and value creation through connected technologies, such as the Internet of Things (IoT) [26,27]. It entails transforming processes and systems into digital workflows that enhance operational performance, innovation, and accessibility [13,15,18]. Unlike digitization, digitalization redefines business models rather than merely converting them into digital ones.

Digital transformation refers to the strategic implementation of digital technologies across all organizational functions, revolutionizing operational frameworks, value delivery, and market engagement [28,29]. It is driven by technologies such as AI, big data, cloud computing, and mobile platforms to foster agility, customer experience, and innovation [20,30,31,32]. Successful transformation requires cultural adaptation and data-driven decision-making capabilities [13,33,34,35].

In the industrial domain, the integration of digital technologies with traditional manufacturing systems marks the onset of the Fourth Industrial Revolution, also known as Industry 4.0 [11,36]. This transformation, aligned with the broader digital transformation, is reshaping global industry landscapes [37,38]. Scholars emphasize that Industry 4.0 constitutes the foundational layer of industrial digital transformation, enhancing value creation through interconnected, data-driven systems [3,39]. As building blocks of digital transformation, Industry 4.0 technologies enhance live data exchange and collaboration across the value chain [34,40]. Additionally, Industry 4.0 technologies facilitate sustainable business models by enabling real-time data analytics, automation, and digital integration, which enhance resource efficiency and reduce operational waste [6]. In the food sector, digital tools such as IoT, AI, and blockchain support circular economy practices by optimizing supply chains, minimizing waste, and improving traceability [2]. Collectively, these advancements underscore how digital transformation fosters environmentally responsible and economically resilient production systems. Therefore, both concepts are inherently linked and central to sustaining competitiveness and innovation in the digital economy [11]. Numerous companies require assistance in transforming their businesses for the digital era, particularly manufacturing firms [11,41,42]. Hence, it is essential to comprehend the paradigm shifts associated with each level of Industry 4.0.

2.2. Frameworks and Models for Assessing Industry 4.0 Readiness

Industry 4.0 is reshaping the manufacturing environment. A smart factory exemplifies the core principles and implementation of Industry 4.0. The boundaries between the real and the virtual factory (digital twin) were erased by connected, embedded, optimized systems using CPS and IoT [22,43,44,45]. The data-driven processes of real-time communication are supported by industrial automation, intelligent robotics, big data analytics, cloud computing, artificial intelligence (AI), and cybersecurity [1,2,46]. These technologies aim to connect all devices, machines, and systems where the data are stored, transmitted, digitized, and processed to create an intelligent workflow and real-time monitoring [47,48,49].

Additionally, Industry 4.0 has strengthened horizontal integration across supply networks, enabling real-time communication that connects smart factories, smart products, suppliers, and customers, thereby enhancing the entire value chain [8,50]. The integration of digital twin technology across the project lifecycle exemplifies such dynamic capabilities, allowing organizations to adapt to complex environmental challenges and shifting stakeholder demands [45]. Beyond improving competitiveness, this adaptive capacity enhances long-term sustainability by promoting efficient resource utilization, fostering stakeholder collaboration, and enabling continuous innovation.

According to [51], the digital transformation roadmap begins with the company’s self-evaluation to understand its digital maturity level. Ref. [52] defines digital maturity as the combination of digital intensity (investment in technology to transform operational processes within the organization) and transformation management intensity (strengthening internal capacities to enable enterprise-wide digital transformation). Ref. [53] determines that digital maturity represents a dynamic and perpetual process of adaptation driven by the ever-evolving nature of the digital landscape. Ref. [1] noted that determining the level of Industry 4.0 readiness serves as a foundational step in the development process, preceding engagement in the maturation phase. However, the Industry 4.0 maturity assessment focuses on measuring progress and promoting continuous improvement. Recent studies have demonstrated the emergence of readiness and maturity models in the production domain, including strategic guidance and roadmap elements for Industry 4.0 [1,43]. For instance, models and tools are provided by IMPULS, ACATECH, VDMA, PwC, and Industry4WRD Readiness Assessment, as well as the Singapore Smart Industry Readiness Index (SIRI) and the Thailand I4.0 Index. These models are presented in Table 1.

A comparison of existing studies on the Industry 4.0 model reveals several shared dimensions, which serve as enablers for organizational transformation. There are five shared dimensions, including organization, business value chain, strategy, technology capability, and human resource capability [7]. These dimensions closely align with the principles of dynamic capability theory, as they contribute to a firm’s ability to sense opportunities, seize innovations, and reconfigure resources in rapidly evolving environments [54]. Hence, Industry 4.0 dimensions not only represent technological advancements but also function as foundational mechanisms that foster organizational agility and sustained competitiveness through dynamic capabilities. The maturity stages of Industry 4.0 are classified based on the level of industrial revolution implementation in a smart factory. The existing studies on the Industry 4.0 model are limited in their comprehensive framework, which integrates various dimensions of Industry 4.0 readiness, particularly in SME- specific guidelines. The authors conclude that most dimensions of technology adoption, strategic HRD, and organizational involvement require leadership and a strategic need for additional elaboration [55]. The study by [56] emphasizes the importance of the practical implementation and scalability of digital technologies across various sectors in transforming manufacturing toward Industry 4.0. To enhance a higher level of Industry 4.0, effective HRD is one critical digital transformation strategy to boost overall productivity and competitiveness [57].

Table 1. Comparative analysis of Industry 4.0 readiness and maturity frameworks.

Maturity and Readiness Models	Focus	Gaps
IMPULS— Industrie 4.0 Readiness [49]	A robust framework of Industry 4.0 readiness to evaluate the current digital maturity for SMEs. The proposed maturity model comprises six distinct phases: outsider, beginner, intermediate, experienced, expert, and top performer. Industry 4.0 readiness is characterized across six dimensions: employees, strategy and organization, smart products, data-driven services, smart operations, and the smart factory.	The industry-specific roadmaps and the implementation steps are limited in scope.
A maturity model for evaluating Industry 4.0 readiness and the maturity levels of manufacturing enterprises [1]	The framework guiding Industry 4.0 implementation encompasses nine key dimensions, prioritizing human factors, including people, leadership, culture, governance, strategy, and customers, alongside operational elements such as products, operations, and technology. A demonstration of the calculation process for maturity assessment indicators is provided.	The perspective of SMEs regarding Industry 4.0 is limited. Inadequacy of a maturity model for Industry 4.0.
ACATECH— Industrie 4.0 Maturity Index [58]	A comprehensive maturity model assessment of the current state and the steps towards Industry 4.0. The maturity model outlines six progressive stages ranging from computerization to adaptability: computerization, connectivity, visibility, transparency, predictive capacity, and ultimately adaptability. The digitalization framework integrates four critical dimensions: organizational culture, structure, resources, and information systems. Presents the methodology applied through a case study.	The practical implementation strategies and SME-specific guidelines are limited in scope.
VDMA—Guidelines for Industry 4.0 [59]	Explores the formulation of an Industry 4.0 vision for SMEs. A toolbox outlining various characteristics and technologies relevant to Industry 4.0 has been provided. The toolbox outlines a five-phase implementation process: preparation, analysis, creativity, evaluation, and execution. It is important to note that these phases are separate from the maturity model levels.	Limited focus on non-mechanical sectors. The evaluation of Industry 4.0 readiness and practical implementation steps remain to be incorporated.
PWC—Readiness model [51]	The Industry 4.0 maturity model defines five progressive levels: digital novice, vertical integrator, horizontal integrator, collaborator, and digital champion. A seven-dimensional framework is proposed, encompassing organization employees and digital culture, digital business models and customer access, compliance with security, legal, and tax requirements, data and analytics as core capabilities, agile IT architecture, the digitization of product and service offerings, and the integration of vertical and horizontal value chains.	An SME perspective for Industry 4.0 is dismissed. Guidance on incremental improvement toward Industry 4.0 is missing.
Industry4WRD Readiness Assessment [60]	A national framework for guiding manufacturing firms towards Industry 4.0 has been demonstrated. Considers three shift factors for assessing capabilities and readiness of Industry 4.0 implementation: technology adoption, process optimization, and workforce upskilling. Evaluating the readiness level in percentage score.	The practical implementation strategies and SME-specific guidelines are limited in scope. The sector-specific strategies within the Industry 4.0 framework remain inadequately defined, limiting their effective application.
Singapore Smart Industry Readiness Index (SIRI) [61]	A framework for evaluating current readiness and progress in Industry 4.0 has been developed. Three building blocks, organization, process, and technology, are considered essential to facilitate the complete implementation of Industry 4.0. It outlines a four-step process that is continuous towards Industry 4.0 transformation, known as the LEAD framework: learn, evaluate, architect, and deliver.	The perspective of SMEs regarding Industry 4.0 has been overlooked. The steps necessary to achieve the Industry 4.0 framework are missing.
Thailand I4.0 Index [62]	A six-dimensional Industry 4.0 model framework has been developed, encompassing technology, smart operation, IT systems, data transactions, human capital, and market and customer considerations. A six-level shift towards Industry 4.0 has developed for each key dimension. The assessment tool focuses on readiness in key sectors of automotive, electronics, and food processing.	Lacks comprehensive frameworks for diverse industries. Practical implementation guidelines for SMEs are limited.

Table 1. Comparative analysis of Industry 4.0 readiness and maturity frameworks.

Maturity and Readiness Models	Focus	Gaps
IMPULS— Industrie 4.0 Readiness [49]	A robust framework of Industry 4.0 readiness to evaluate the current digital maturity for SMEs. The proposed maturity model comprises six distinct phases: outsider, beginner, intermediate, experienced, expert, and top performer. Industry 4.0 readiness is characterized across six dimensions: employees, strategy and organization, smart products, data-driven services, smart operations, and the smart factory.	The industry-specific roadmaps and the implementation steps are limited in scope.
A maturity model for evaluating Industry 4.0 readiness and the maturity levels of manufacturing enterprises [1]	The framework guiding Industry 4.0 implementation encompasses nine key dimensions, prioritizing human factors, including people, leadership, culture, governance, strategy, and customers, alongside operational elements such as products, operations, and technology. A demonstration of the calculation process for maturity assessment indicators is provided.	The perspective of SMEs regarding Industry 4.0 is limited. Inadequacy of a maturity model for Industry 4.0.
ACATECH— Industrie 4.0 Maturity Index [58]	A comprehensive maturity model assessment of the current state and the steps towards Industry 4.0. The maturity model outlines six progressive stages ranging from computerization to adaptability: computerization, connectivity, visibility, transparency, predictive capacity, and ultimately adaptability. The digitalization framework integrates four critical dimensions: organizational culture, structure, resources, and information systems. Presents the methodology applied through a case study.	The practical implementation strategies and SME-specific guidelines are limited in scope.
VDMA—Guidelines for Industry 4.0 [59]	Explores the formulation of an Industry 4.0 vision for SMEs. A toolbox outlining various characteristics and technologies relevant to Industry 4.0 has been provided. The toolbox outlines a five-phase implementation process: preparation, analysis, creativity, evaluation, and execution. It is important to note that these phases are separate from the maturity model levels.	Limited focus on non-mechanical sectors. The evaluation of Industry 4.0 readiness and practical implementation steps remain to be incorporated.
PWC—Readiness model [51]	The Industry 4.0 maturity model defines five progressive levels: digital novice, vertical integrator, horizontal integrator, collaborator, and digital champion. A seven-dimensional framework is proposed, encompassing organization employees and digital culture, digital business models and customer access, compliance with security, legal, and tax requirements, data and analytics as core capabilities, agile IT architecture, the digitization of product and service offerings, and the integration of vertical and horizontal value chains.	An SME perspective for Industry 4.0 is dismissed. Guidance on incremental improvement toward Industry 4.0 is missing.
Industry4WRD Readiness Assessment [60]	A national framework for guiding manufacturing firms towards Industry 4.0 has been demonstrated. Considers three shift factors for assessing capabilities and readiness of Industry 4.0 implementation: technology adoption, process optimization, and workforce upskilling. Evaluating the readiness level in percentage score.	The practical implementation strategies and SME-specific guidelines are limited in scope. The sector-specific strategies within the Industry 4.0 framework remain inadequately defined, limiting their effective application.
Singapore Smart Industry Readiness Index (SIRI) [61]	A framework for evaluating current readiness and progress in Industry 4.0 has been developed. Three building blocks, organization, process, and technology, are considered essential to facilitate the complete implementation of Industry 4.0. It outlines a four-step process that is continuous towards Industry 4.0 transformation, known as the LEAD framework: learn, evaluate, architect, and deliver.	The perspective of SMEs regarding Industry 4.0 has been overlooked. The steps necessary to achieve the Industry 4.0 framework are missing.
Thailand I4.0 Index [62]	A six-dimensional Industry 4.0 model framework has been developed, encompassing technology, smart operation, IT systems, data transactions, human capital, and market and customer considerations. A six-level shift towards Industry 4.0 has developed for each key dimension. The assessment tool focuses on readiness in key sectors of automotive, electronics, and food processing.	Lacks comprehensive frameworks for diverse industries. Practical implementation guidelines for SMEs are limited.

2.3. Strategic HRD in the Context of Industry 4.0

According to the digital transformation policy perspective of Douglas Frantz, Secretary-General of the Organization for Economic Cooperation and Development, digital transformation policy has been identified as having three foundational pillars [63]. First, the integrated policy framework involves a comprehensive evaluation of digital development through both quantitative and qualitative metrics, identifying key economic and societal drivers of transformation. Second, the targeted policy analysis focuses on the specific effects of digital transformation, including its impact on employment, international trade, taxation, and the distribution of benefits. Third, the core modules for addressing digital age challenges comprise workforce skills, market competition, welfare enhancement, and integrating digital technologies into policy formulation, implementation, and reform [63]. The theoretical framework guiding this research posits that an organization’s strategic HRD is important in enhancing Industry 4.0 readiness. In the research, an organization’s HRD strategies are used as strategic HRD in the rest of the paper. Drawing on the literature and the identified gaps, this study proposes several key factors that influence a strategic HRD approach toward Industry 4.0 readiness.

2.3.1. Organizational Preparedness for HRD in Industry 4.0

Human capital development is widely recognized as a critical enabler of Industry 4.0 implementation, as it facilitates the adoption and integration of advanced technologies across organizational systems. Strategic HRD, encompassing continuous training, skill enhancement, and the cultivation of a learning-oriented and innovative culture, has been shown to significantly improve Industry 4.0 readiness [56,63]. For instance, the study of [64], a case study on the Chinese construction industry, demonstrated that digital transformation strategies combined with ongoing workforce development initiatives enhanced both operational efficiency and competitive positioning.

The development of human capital is not limited to large-scale industrial environments but is also crucial in micro and small enterprise contexts. Ref. [65] draws on Social Cognitive Theory, emphasizing the importance of human resource management competencies, including recruitment, training, and performance management, in building entrepreneurial self-efficacy and sustaining business continuity in resource-constrained settings. Similarly, ref. [63] argues that collaboration with educational institutions and structured training pathways is essential for equipping the workforce with the digital and technical skills necessary for transformation. These efforts align with [66], who advocate for a multidimensional HRD framework that combines digital fluency with interpersonal capabilities, such as leadership, adaptability, and communication. Overall, the reviewed literature underscores that effective human resource development strategies for Industry 4.0 require the integration of technical expertise, soft skill development, and psychological resilience to cultivate a workforce that is adaptable, innovative, and prepared for future challenges. Empirical evidence further indicates that organizations advancing in Industry 4.0 readiness have done so through strategic HRD initiatives focused on continuous learning, skill advancement, and the promotion of innovation-oriented organizational cultures [56,63].

In previous Industry 4.0 readiness models, HRD focused on incremental skill enhancements rather than holistic workforce transformation. These traditional HRD methods often fall short in the context of Industry 4.0 due to their reactive nature and lack of integration with continuous technological advancements [55]. The competencies needed for Industry 4.0 extend beyond technical abilities, including critical soft skills. Employees must be proficient in digital literacy, advanced technical skills, and soft skills, including collaboration, adaptability, and cybersecurity awareness [67]. Organizational structures may also need to adapt to facilitate cross-functional collaboration and rapid decision-making. A more proactive and integrated HRD strategy is required to address immediate skill gaps and foster continuous learning and innovation.

2.3.2. The Role of Leadership in Enabling Industry 4.0 Transformation

Leadership plays a crucial role in shaping the direction of strategic HRD within the context of Industry 4.0. Effective leadership is essential for organizations investing in employee training and development programs tailored to the demands of Industry 4.0 technologies and skills. For SMEs, leaders are typically the owners who play a significant role as practitioners, bridging the gap between theory and reality. They manage all aspects of a company and respond to innovation [68]. Ref. [69] emphasizes that effective leadership in the digital era demands specific skills and attributes. These competencies are essential for guiding organizations through the complexities of transformation. Additionally, leaders must prioritize the reskilling and upskilling of their workforce to prepare them for digital transformation, which includes developing digital skills crucial for the adoption and effective use of new technologies [70]. Key factors for successful leadership in digital transformation include empowering employees, demonstrating digital expertise, maintaining flat hierarchies, and actively engaging in partnerships and ecosystems [69].

In addition to training, leadership is also responsible for supporting employees as they adapt to novel technologies and processes associated with Industry 4.0. The process involves both resource provision and the development of a work environment that motivates employees to accept change and pursue innovation. The role of leadership in overcoming barriers to digital transformation is critical, as it directly impacts the organization’s ability to successfully navigate the complexities of Industry 4.0 [71].

Moreover, effective leadership ensures that employees are actively involved in the Industry 4.0 adoption process. Engaging employees in decision-making and implementation fosters a sense of ownership and buy-in, which is crucial for successfully adopting new technologies. Leaders who prioritize communication and employee involvement create a culture of collaboration that enhances the overall effectiveness of HRD strategies [72].

However, leaders need help building human resource capabilities for Industry 4.0 adoption. These challenges include aligning training programs with rapidly evolving technological requirements and ensuring that employees possess the necessary digital capabilities. Leaders must continually evaluate the effectiveness of HRD programs to ensure they align with the strategic goals of Industry 4.0 adoption, making adjustments as needed to address gaps in training and development [73].

2.3.3. Strategic HRD Adaptation in Response to Business Pressures: Enhancing Industry 4.0 Preparedness

The increasing pressures on businesses due to the global shift towards Industry 4.0 have led to significant transformations in strategic HRD. These pressures arise from achieving operational benefits, ensuring quality consistency, meeting increased demand, and capitalizing on market opportunities. Additionally, international competition, rising labor costs, and evolving customer requirements further compel organizations to adapt their human resource development (HRD) strategies to remain competitive.

Operational benefits, including time-saving, optimized productivity outcomes, cost-effectiveness, and flexibility, are key drivers of Industry 4.0 adoption. The integration of enterprise resource planning (ERP) and strategic management systems in industries like food production in Brazil exemplifies how organizations seek to optimize their processes and reduce non-value-adding efforts through digital transformation [74]. These operational improvements are crucial for organizations seeking to boost productivity and optimize their operations within Industry 4.0.

Quality consistency is another critical factor driving HRD strategies. The demand for high-quality products necessitates that organizations develop robust training programs to ensure their workforces are equipped with the necessary skills to maintain quality standards. This is particularly important in sectors such as pharmaceuticals, where maintaining consistent quality is crucial for competitiveness and customer trust [75].

Market opportunities, particularly those involving the expansion into new export markets, significantly influence HRD strategies. Organizations must ensure that their workforce is prepared to meet international standards and navigate the complexities of global markets. This requires continuous learning and development initiatives that equip the workforce with the requisite knowledge and skills to adapt to market demands [76].

International competition and higher labor costs further exacerbate the need for strategic HRD. As businesses face increasing competition from global players, they must innovate and improve their operational processes to stay competitive. HRD strategies must, therefore, focus on developing a workforce that is not only technically proficient but also adaptable and innovative, capable of meeting the challenges posed by Industry 4.0 [75].

Lastly, customer requirements have evolved significantly, demanding more personalized, high-quality products. Organizations must ensure that their employees are trained in the latest manufacturing techniques to meet these demands and can deliver products that meet customer expectations. This focus on customer-centric HRD strategies is crucial for businesses aiming to succeed in the Industry 4.0 era [77].

2.3.4. Technological Readiness as a Pillar of Industry 4.0 Adoption

The effective execution of Industry 4.0 strategies relies heavily on integrating advanced digital technologies that drive transparency, optimization, and automation across production processes. Digital twin technology is a key enabler of this transformation, providing real-time visibility into manufacturing operations and enabling continuous monitoring and optimization. This capability improves production efficiency and reduces operational disruptions, making it a cornerstone of modern manufacturing [78,79].

In addition to transparency, digital technologies significantly optimize logistics and production processes. These technologies streamline operations and minimize waste by leveraging real-time data from IoT sensors, thus improving overall productivity [80]. Building on this, ref. [45] underscores that while digital twin technology holds transformative potential in construction, its adoption is constrained by disparities in digital maturity and organizational inertia. The incorporation of enabling technologies, including cloud computing, building information modeling (BIM), the Internet of Things (IoT), augmented reality, and virtual reality, across the project lifecycle is identified as essential for enhancing collaboration, sustainability, and digital performance. Comparable advancements are evident in the food industry, where [2] demonstrates how digital twins, artificial intelligence, and blockchain technologies enhance supply chain transparency, improve energy efficiency, and facilitate personalized production. Furthermore, digital twins and smart manufacturing systems enable dynamic monitoring and optimization of energy consumption, facilitating sustainable and cost-efficient operational practices [81].

Automatic production control is another critical area where digital technologies have a profound impact. By enabling real-time adjustments to production parameters, these technologies ensure consistent product quality and reduce the need for human intervention [82]. Additionally, integrating VR/AR/MR technologies with digital twins provides immersive training and simulation environments, further enhancing the effectiveness of production and maintenance processes [83].

These advancements in digital technology improve operational efficiency and necessitate a corresponding evolution in strategic HRD. As organizations adopt these technologies, there is a growing need to align workforce skills and capabilities with the demands of Industry 4.0. This alignment underscores the importance of developing HRD strategies tailored to support industries’ digital transformation.

These four factors are hypothesized to have a positive and significant impact on HRD strategies toward Industry 4.0 readiness. The proposed framework provides a basis for understanding the complex interplay between these factors and offers a foundation for empirical investigation.

2.4. A Dynamic Capability Framework for Strategic HRD and Industry 4.0 Readiness

The theoretical framework guiding this research is grounded in dynamic capability theory. In the first step, the research conducts case studies on digital transformation in Thai manufacturing firms using the Industry 4.0 readiness model. These case studies serve as practical lessons learned from firms of different sizes, aiming to identify critical resources and examine how firms transform these resources to enhance their Industry 4.0 maturity. In the second step, the findings from the case studies are synthesized and integrated into academic theory to design a survey that explores the proposed pivoting framework. Consequently, a mixed-method approach is employed to derive essential insights that support entrepreneurs in developing strategic goals and preparing guidelines tailored to each level of Industry 4.0. In the context of Industry 4.0, the development of dynamic capabilities and strategic HRD is a valuable resource that enables firms to acquire and assimilate new knowledge and skills essential for technological adoption and innovation. Dynamic capability theory emphasizes the firm’s ability to adjust and realign its resources and capabilities in response to an evolving environment [84]. Strategic HRD plays a crucial role in building dynamic capabilities by fostering a culture of learning, innovation, and agility within the organization. Ref. [85] further emphasizes that dynamic capabilities, particularly sensing, seizing, and transforming, significantly impact human resource performance by enabling firms to better understand employee needs, enhance job satisfaction, and support continuous skills development. The integration of collaborative technologies such as cobots facilitates the reconfiguration of work processes and empowers employees, thereby contributing to improved talent retention and workforce adaptability. In alignment with the dynamic capability theory, ref. [6] emphasizes that organizations must reconfigure their internal processes and adapt quickly to external shifts to maintain competitiveness in the Industry 4.0 environment. Their findings suggest that strategic HRD, operationalized through digital human resource management, facilitates continuous learning, customized training, and digital skill acquisition, key enablers of workforce agility and innovation. Moreover, the study highlights the importance of management support and sustainable human resource practices in promoting employee engagement, thereby enhancing the organization’s ability to adapt and retain talent in the face of technological change.

Drawing upon these theoretical lenses, this study proposes a conceptual model that examines the interplay between several key factors influencing an organization’s HRD strategies and its readiness for Industry 4.0. These factors, identified from the literature and the gaps discussed in previous sections, are as follows:

Industry 4.0 HRD Preparedness: This factor reflects the organization’s commitment to developing a workforce with the skills and competencies necessary for Industry 4.0. It encompasses technical skills related to digital technologies, as well as soft skills such as adaptability, problem-solving, and collaboration. HRD preparedness also includes the organization’s ability to create a learning environment that supports continuous skill development and knowledge acquisition.

Leadership Support for Industry 4.0: This factor emphasizes the role of leadership in driving Industry 4.0 initiatives. It includes the leadership’s vision for digital transformation, active support for HRD programs, and the ability to communicate a clear strategic roadmap for technological adoption. Effective leadership is essential for mobilizing resources, aligning organizational goals, and fostering a culture of innovation that supports Industry 4.0 readiness.

Business Pressures: This factor acknowledges the influence of the business environment on HRD strategies, encompassing pressures arising from achieving operational benefits, maintaining quality consistency, meeting increasing demand, and addressing competitive challenges. These external pressures can catalyze HRD initiatives, prompting organizations to invest in workforce development to meet evolving market demands and maintain competitiveness.

Technological Readiness for Industry 4.0: This factor focuses on the organization’s technological capabilities and infrastructure, including the adoption of digital technologies that enhance transparency, optimize processes, and enable automation and real-time control in production. Technological readiness provides the foundation for successful Industry 4.0 implementation, creating opportunities for HRD to focus on developing skills that complement and leverage these technologies.

The interplay between these factors is complex and dynamic. For instance, leadership support can influence HRD preparedness by allocating resources for training and development programs. Similarly, external business pressures can shape technological readiness by necessitating investments in digital infrastructure. This study aims to empirically investigate these relationships and assess their combined impact on HRD strategies and Industry 4.0 readiness (Figure 1).

• Hypotheses

Building upon the theoretical framework and literature survey, the following hypotheses are presented:

Hypothesis 1a: 

Business pressures positively affect Industry 4.0 HRD preparedness by driving organizations to adapt their training programs to meet operational, quality, market, and competitive demands.

Hypothesis 1b: 

Business pressures positively affect leadership support for Industry 4.0 by forcing leaders to implement the Industry 4.0 strategy to achieve workforce digital skills and digital transformation.

Hypothesis 1c: 

Business pressures positively affect technological readiness for Industry 4.0 by driving organizations to implement Industry 4.0-related technologies.

Hypothesis 2a: 

Leadership support for Industry 4.0 has a positive effect on Industry 4.0 HRD preparedness by ensuring adequate investment in employee training, supporting adaptation to new technologies, fostering digital skill development, involving employees in the adoption process, and effectively addressing the challenges of building HR capabilities.

Hypothesis 2b: 

Leadership support for Industry 4.0 affects technological readiness for Industry 4.0 by encouraging the workforce to implement and extend the Industry 4.0 project.

Hypothesis 3: 

Technological readiness for Industry 4.0 positively influences Industry 4.0 HRD preparedness by enhancing transparency, optimizing processes, and enabling real-time control, thereby aligning workforce development with the technological demands of Industry 4.0.

3. Research Methodology

3.1. Data Collection

The data were collected in 2023. The findings remain relevant, particularly within the context of emerging economies, where the adoption of Industry 4.0 technologies tends to progress more gradually and unevenly compared to advanced economies. Structural limitations, institutional constraints, and limited access to resources contribute to extended timelines for digital transformation. Consequently, the strategic and organizational factors examined in the study remain applicable to the current context. Key areas such as HRD practices, technological readiness, business pressures, and leadership support continue to reflect the prevailing conditions and priorities in emerging economies. This reinforces the ongoing value of the insights for both academic research and policy formulation related to Industry 4.0 readiness in developing economies.

3.1.1. Case Study Design and Qualitative Data Procedures

Semi-structured interviews were conducted with top executives and general managers from diverse companies involved in Industry 4.0 initiatives. The participants were intentionally selected based on specific business models, organizational structures, firm sizes, and supply chain networks. The study was conducted through in-depth, semi-structured interviews with executives of target companies to gather information. The target respondents were selected based on the organization’s adoption of Industry 4.0 or digital transformation initiatives. The company size and sector diversity provide a comprehensive understanding of different Industry 4.0 readiness levels [86]. The interviews were conducted onsite at the participants’ companies in July 2023, with each session lasting approximately 45 to 60 min. The interview protocol focused on themes aligned with the research objectives, including HRD practices and challenges, organizational readiness for Industry 4.0, and key enablers of successful digital transformation. Open-ended questions encouraged participants to elaborate on their experiences and strategic responses to technological change.

The interviews were transcribed verbatim and analyzed using a cross-case comparative approach, guided by the Thailand Industry 4.0 Index framework. Rather than employing formal coding techniques, the analysis involved a systematic comparison of responses across cases to identify patterns, similarities, and differences related to key dimensions of Industry 4.0 readiness. Each case was examined in terms of the model’s components, including strategy and organization, smart factory, smart operation, smart product, IT system, data transaction, and human capital, to assess how organizations at different readiness levels approached digital transformation. This method enabled a structured interpretation of the qualitative data while preserving the contextual richness of each company-specific insight.

3.1.2. Survey Design and Quantitative Data Collection Procedures

A non-probability purposive sampling strategy was employed to select informants for the semi-structured interviews. This approach was appropriate given the exploratory nature of the study and the need to gain in-depth insights from individuals with direct experience in implementing Industry 4.0. Participants were selected based on their roles in managing digital transformation and human resource development within manufacturing firms. The target respondents are top executives, HR managers, and general managers in the targeted companies who explore perceptions, experiences, and challenges related to Industry 4.0. The data was collected from August to September 2023 from the Thai manufacturing sector. A structured questionnaire-based survey was distributed to a list of 1155 manufacturing firms, which were obtained from two organizations, namely the Thai Auto Parts Manufacturers Association (TAPMA) and the Industrial Estate Authority of Thailand (IEAT). A total of 144 valid samples have been selected for Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the hypotheses, which represent 12.47% of the respondents.

The final survey sample consisted of respondents representing 12.47% of the target population. While this response rate may limit the generalizability of the findings, it is not uncommon in organizational research involving managerial respondents, where access and availability can constrain participation. Efforts were made to enhance validity by ensuring diversity in firm size, industry type, and managerial roles. Although the sample may not be fully representative of the entire population, the data still provide valuable insights into Industry 4.0 readiness and HRD practices within the studied context. This limitation is acknowledged, and future studies are encouraged to expand the sample for broader applicability.

3.2. Data Analysis

A mixed-method approach was adopted in this research to integrate qualitative and quantitative research methodologies, thereby enhancing Industry 4.0 readiness through strategic human resource development (HRD). This combination provides a comprehensive framework for understanding complex phenomena. Additionally, this approach enables triangulation, thereby strengthening the validity and reliability of the findings [87].

3.2.1. Thematic Analysis and Cross-Case Interpretation

A thematic analysis is adopted to identify common themes and patterns [88], providing nuanced insights into HRD within Industry 4.0 contexts [89]. Then, the results are analyzed via cross-case comparisons. The results from a qualitative methodology are considered in conjunction with a questionnaire-based quantitative method.

3.2.2. Structural Equation Modeling for Industry 4.0 Readiness

Structured surveys were used to examine the factors influencing organizational human resource development (HRD) strategies related to Industry 4.0. Likert-scale questions captured perceptions related to business pressures, leadership support, HRD preparedness, and technological readiness. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed due to its suitability for exploratory research and its ability to model complex relationships among latent constructs [90]. PLS-SEM demonstrates robustness under non-normal data distributions [91]. Additionally, PLS-SEM is particularly effective for predictive modeling and analyzing mediation effects, which are central to this study’s conceptual framework. Descriptive statistics complemented the analysis by providing insights into respondent profiles and distributional characteristics.

3.2.3. Mixed-Method Integration Approach

A convergent parallel mixed-method design was adopted. Qualitative findings informed the survey design, and both datasets were analyzed independently before being integrated during interpretation. This approach enabled triangulation and enriched the analysis by combining contextual insights with empirical validation [87]. During the preparation of this work, ChatGPT GPT-4o and o4-mini was used to improve readability.

4. Results

4.1. Qualitative Results

4.1.1. Firm-Level Case Study Insights on Industry 4.0 Readiness

This section presents the findings from the qualitative analysis, which focused on five case study companies and explored their readiness for Industry 4.0 and strategic HRD practices (

Table 2. Firm demographics and structural profiles in case study sample.

Company	Company 1	Company 2	Company 3	Company 4	Company 5
Year of Establishment	1990	1999	1987	1893	1916
Number of Employees	400	55	190	700	120,726
Company Size	Large	Medium	Medium	Large	Large
Business Type	Manufacturing	Manufacturing	Manufacturing	Manufacturing	Manufacturing
Product	Finished yarn and fabric	Molded plastic parts	Plastic products, Plastic Bags	Sewing machines and components	Vehicles
Suppliers	Local	Local and International	Local and International	International	International
Customers	Multinational	Multinational	Multinational	International	Multinational
Supply Chain Type	Exporter	Multinational	Multinational	Multinational	Multinational

).

Company 1

Company 1, established in 1990, specializes in dyeing and embellishing fibers and fabrics. With 400 employees, it relies on local suppliers and exports to multinational customers. The company requires assistance with budget constraints and perceives Industry 4.0 technology as both expensive and challenging. Their strategy focuses on maintaining production standards and cost reduction, with limited investment in employee development. Training programs could be conducted more frequently, resulting in higher skill levels among employees. This lack of investment hinders their ability to adopt new technologies and remain competitive.

Company 2

Established in 1999, Company 2 manufactures high-quality plastic products with a staff of 55 employees. It utilizes some Industry 4.0 technologies, including robots and real-time monitoring systems. However, it needs a comprehensive Industry 4.0 strategy. The company needs to develop a clear roadmap for technology adoption and enhance employee training programs to improve skill levels. Investment in advanced data analytics tools could further optimize operations and decision-making processes.

Company 3

Company 3, which produces various plastic bags, employs 190 people. About 60% of its employees have some understanding of Industry 4.0. The company continuously learns about and develops its Industry 4.0 adoption, regularly training staff and encouraging continuous education. This proactive approach has led to better preparedness compared to other companies. Employees are regularly trained to stay current with technological trends and evolving customer demands.

Company 4

A large company with 700 employees, Company 4 produces sewing and embroidery machines. It extensively utilizes advanced technologies, including ERP, MES, CAQ, WMS, and cloud systems, positioning it at Level 5 of Industry 4.0 readiness. The company has a well-defined plan for employee development, offering numerous training programs and encouraging knowledge sharing among staff. This comprehensive approach supports high-quality HRD and technological integration.

Company 5

An international company established in 1916, Company 5 specializes in a wide range of vehicles and employs 120,726 people. They have implemented a range of Industry 4.0 technologies, which consist of cloud services, big data analytics, and robotics. The company continually explores advanced technologies, such as AI and machine learning, to optimize its operations. Employee training programs ensure that the workforce is equipped to handle the demands of Industry 4.0, thereby enhancing efficiency and productivity.

4.1.2. Comparative Insights Across Industry 4.0 Case Studies

The thematic analysis reveals distinct levels of Industry 4.0 readiness among the five participating companies, as illustrated in Figure 2. For clarity and comparative insight, each readiness dimension is presented in a separate table.

Table 3. Strategy and organization readiness: a comparative study based on the Thailand Industry 4.0 Index.

Company	Industry 4.0 Readiness Level	Company’s Practices
Company 1	Level 0	An established strategy has yet to be established. However, plans are in place for the development of future standards.
Company 2	Level 1	ISO 9100, Original Equipment Manufacturer (OEM), and Original Design Manufacturer (ODM) are all part of the company’s plan to develop Industry 4.0 policies. The organization intends to commence capital investments in Industry 4.0.
Company 3	Level 1	Under ISO 9001 standards, the organization intends to engage in collaborative efforts with Japanese entities. Additionally, the company has developed strategic plans to invest in Industry 4.0 technologies. These investments are poised to significantly improve operational efficiency and strengthen the company’s competitiveness in the global market.
Company 4	Level 5	The strategic plan for production adjustment focuses on technological advancement and operational efficiency. The enterprise-wide innovation management system has been successfully implemented throughout the organization.
Company 5	Level 5	In 2018, the organization initiated the implementation of Industry 4.0. Key performance indicators were established, accompanied by substantial investments in research and development. The strategy mentioned earlier has already been implemented, establishing an enterprise-wide innovation management system.

summarizes the findings related to the strategy and organization dimension, while

Table 4. Smart factory readiness: a comparative study based on the Thailand Industry 4.0 Index.

Company	Industry 4.0 Readiness Level	Company’s Practices
Company 1	Level 0	The prerequisites still need to be fulfilled.
Company 2	Level 2	Injection molding machines, servo-controlled machines, and robotic computer-controlled systems. Data collected and utilized for specific production procedures and in various company divisions support information technology systems.
Company 3	Level 1	Employs Graco’s Matrix Total Fluid Management System. The existing equipment infrastructure partially meets the prospective requirements.
Company 4	Level 5	Employs Coordinate Measuring Machines (CMM) for three-dimensional measurements and automation for inspection purposes. All data is systematically collected and utilized through an IT system managed by qualified professionals.
Company 5	Level 5	Highly skilled and experienced in operations, utilizing robotics and machine learning technologies. All data is meticulously gathered and employed through an information technology system managed by experts in the field.

presents the smart factory dimension. The smart operation dimension is detailed in

Table 5. Smart operation readiness: a comparative study based on the Thailand Industry 4.0 Index.

Company	Industry 4.0 Readiness Level	Company’s Practices
Company 1	Level 1	At the outset, the system-integrated information dissemination.
Company 2	Level 3	Real-time monitoring, IoT development, and cloud data management. The initial solution was designed for cloud-based software, data storage, and data analysis.
Company 3	Level 1	It employs ERP for seamless inventory updates and plans to integrate AI. At the foundational level, the system combines information sharing to streamline operations.
Company 4	Level 5	Comprehensive ERP systems. Cloud-based IoT solutions. Human augmentation in manufacturing processes. Power BI for data visualization and analytics. An Android-based mobile application system for inventory management and seamless integration with manufacturing facilities. System integration services for disparate technological platforms. Cloud-based software solutions for enhanced efficiency. Implementation of autonomous control systems for increased automation.
Company 5	Level 5	Systematic incorporation of digitalized operations and cloud-based data storage mechanisms. Seamless integration and harnessing of cloud-based software applications. Effective deployment of autonomous control systems to optimize operational efficiency.

, followed by the smart product dimension in

Table 6. Smart product readiness: a comparative study based on the Thailand Industry 4.0 Index.

Company	Industry 4.0 Readiness Level	Company’s Practices
Company 1	Level 0	The organization needs more preparedness.
Company 2	Level 0	The organization needs more preparedness.
Company 3	Level 0	The organization needs more preparedness.
Company 4	Level 3	The product is equipped with functionalities that enable the linking and collection of data, facilitating subsequent analysis and interpretation.
Company 5	Level 5	The product encompasses a wide range of functionalities. It comprehensively utilizes collected data.

.

Table 7. IT system and data transaction readiness: a comparative study based on the Thailand Industry 4.0 Index.

Company	Industry 4.0 Readiness Level	Company’s Practices
Company 1	Level 0	Failure to employ data analysis techniques and the absence of data integration across various domains.
Company 2	Level 1	Implement data-driven techniques judiciously within the organization’s confines.
Company 3	Level 0	Neglecting the application of data analysis and failing to establish interconnections between data within each domain.
Company 4	Level 5	Utilize comprehensive data-driven approaches, such as data analysis for sales, and leverage cloud-based data collection methods.
Company 5	Level 5	Utilize comprehensive data-driven approaches, such as data analysis for sales forecasting, and maintain a centralized repository of collected data on the cloud.

outlines the IT systems and data transaction dimension, and

Table 8. Human capital readiness: a comparative study based on the Thailand Industry 4.0 Index.

Company	Industry 4.0 Readiness Level	Company’s Practices
Company 1	Level 2	Restricted training, mentor-guided learning. Employees require more specialized skill levels in limited relevant areas due to a lack of targeted training programs.
Company 2	Level 2	Fundamental competencies, which are often acquired without formal instruction, are predominantly developed through supervisor-led training initiatives. Due to insufficient training programs, employees require greater proficiency in relevant domains.
Company 3	Level 3	Within the context of Industry 4.0, approximately 60% of employees comprehend the underlying principles and concepts. These employees possess sufficient skill proficiency in select relevant domains, albeit with limited exposure to structured training programs.
Company 4	Level 5	Extensive training programs, independent study, and knowledge exchange opportunities. Various pertinent areas offer a comprehensive range of skills thanks to a thorough training curriculum.
Company 5	Level 5	Optimized Workflow: Streamlined processes and efficient task execution. Knowledge Dissemination: Transfer of expertise and best practices across teams. Incentive and Recognition: Motivation through rewards and acknowledgments. Interdepartmental Collaboration: Synergy and knowledge sharing among different departments. Skill Enhancement and Academic Partnerships: Professional development opportunities and partnerships with educational institutions. Customized Career Growth: Personalized career plans and progression pathways. Continuous Learning Culture: Commitment to ongoing learning and improvement. Intergenerational Knowledge Exchange: Bridging the gap between experienced and emerging professionals. Regular Project Involvement: Practical application of skills through projects. Cross-functional development: Exposure to various areas and functions. University Collaboration: Partnerships with academic institutions for research and innovation. Comprehensive Skill Set: Access a wide range of relevant skills through extensive training programs.

presents the human capital dimension. Companies 4 and 5 exhibit the highest readiness levels, with comprehensive strategies, significant technological investment, and robust HRD programs. These companies demonstrate a clear commitment to continuous learning, technological integration, and employee empowerment, all of which are critical for achieving Industry 4.0 readiness.

However, Companies 1, 2, and 3 exhibit lower readiness levels due to budget constraints, a lack of strategic planning, and insufficient training programs. These companies must develop clear Industry 4.0 roadmaps, invest in relevant technologies, and enhance employee skills through regular training and professional development initiatives.

The findings underscore the critical role of strategic HRD in enhancing Industry 4.0 readiness. Effective HRD practices, including continuous learning, upskilling, and fostering an innovation-oriented culture, enable organizations to adapt to technological disruptions and sustain long-term competitiveness. Notably, qualitative insights reveal a strong alignment between HRD and economic sustainability goals, as firms that strategically invest in employee development are more capable of achieving operational efficiency and innovation-driven growth. However, tensions emerge in the social sustainability domain, particularly where workforce automation leads to job displacement without adequate reskilling pathways. These insights suggest that while HRD can act as a bridge toward sustainable transformation, its effectiveness hinges on equitable access to training and inclusive learning policies that address both productivity and social equity imperatives.

4.2. Quantitative Results

This study aimed to predict multiple pathways and explore concurrently the relationships between various independent and dependent variables. For the data analysis, we applied PLS-SEM, a method particularly suited for exploratory and predictive modeling, as recommended by [90]. PLS-SEM, also known as variance-based SEM, is particularly effective in handling datasets with missing values and non-normal distributions [90]. Additionally, PLS-SEM offers advantages when working with limited sample sizes. Refs. [92,93] advise that the sample size should exceed ten times the largest count of structural paths associated with any construct in the model. Other studies have demonstrated that PLS-SEM can yield robust results even with small to moderate sample sizes, supporting its application in various fields [94]. We have collected samples from 157 firms, and 144 firms have provided useful and complete information for this research. Thus, our dataset is sufficient for hypothesis testing using Partial Least Squares Structural Equation Modeling (PLS-SEM).

4.2.1. Descriptive Characteristics of Sampled Firms

A summary of the characteristics of 144 respondents is shown in

Table 9. Demographic profile of surveyed manufacturing firms.

Demographic	Descriptive	n	Percent
The company’s size	Small (<50 employees)	58	40.28%
	Large (≥200 employees)	51	35.42%
	Medium (50–199 employees)	35	24.31%
Main Business Activity	Other manufacturing	34	23.61%
	Automobile, auto parts	25	17.36%
	Food, beverages, tobacco	17	11.81%
	Plastic and rubber products	11	7.64%
	Paper, paper products, printing	8	5.56%
	Produce more than one main product	8	5.56%
	Machinery, equipment, tools	7	4.86%
	Textiles	5	3.47%
	Wood, wood products	4	2.78%
	Iron, steel	4	2.78%
	Non-ferrous metals	4	2.78%
	Other electronics and components	4	2.78%
	Handicraft	4	2.78%
	Chemicals, chemical products	3	2.08%
	Coal, petroleum products	2	1.39%
	Metal products	2	1.39%
	Computers and computer parts	2	1.39%
The company’s capital structure	Thai-owned	112	77.78%
	Foreign-owned (MNC)	15	10.42%
	Joint Venture (JV)	17	11.81%

. Categorized by the number of employees, the respondents were divided into small-sized firms (40.28%), medium-sized firms (24.31%), and large-sized firms (35.42%). According to business activities, the “other” manufacturing sector is the most common, comprising 23.61% of the sample. Other significant industries include automobile auto parts at 17.36% and food, beverages, and tobacco at 11.81%. Regarding capital structure, the companies are categorized as 100% Thai-owned, 100% foreign-owned (MNC), and joint ventures (JV). Most companies are 100% Thai-owned, comprising 77.78% of the sample, while 100% foreign-owned companies account for 10.42%, and joint ventures represent 11.81%.

4.2.2. Assessment of Common Method Variance

The outcomes of this research could be affected by common method bias (CMB), as the data were collected from each participant within the same time frame [95]. To ensure validity and manage CMB, we ensured the following: (i) the questionnaire was designed carefully to eliminate confusing phrases and vague terms by generating basic, explicit, and intelligible questions; (ii) all participants were informed that the data obtained would be used strictly for academic research and completely anonymized; (iii) the consistency and transparency of interpretations were ensured by meticulous data collection processes. After data collection, statistical methods were employed to address the challenges of CBM. First, Harman’s single-factor test was applied to explore common method variance. The total variance of 40.73% accounted for the first factor. This remains beneath the pivotal threshold of 50% [95]. Then, we included the constructs in the PLS model and employed a full collinearity test to explore the CMB in the study model. In a thorough assessment of common method bias (CMB), a model is considered free from such bias if all variance inflation factors (VIFs) within the inner model, obtained through a comprehensive collinearity test, fall below the threshold of 3.3 [96]. Our findings revealed that all VIFs were below the cut-off value of 3.3. Thus, the CBM issue was not addressed in this research.

4.2.3. Measurement Reliability and Validity Assessment

A comprehensive evaluation of the measurement model was conducted through various reliability and validity assessments. This included examining indicator reliability, assessing internal consistency, and verifying both convergent and discriminant validity [97]. These evaluations were critical to ensuring the robustness and accuracy of the measurement framework. Indicator reliability is reflected in the loadings of each item, with a recommended threshold of ≥0.70, ensuring that the construct explains at least 50% of the indicator’s variance. The results of the measurement model are presented in

Table 10. Construct measurement and reliability statistics for PLS-SEM.

Constructs	Items	Item Loading	α	rho_A	CR	AVE
Business Pressures	BPS		0.894	0.906	0.918	0.653
– Operational benefits (e.g., cost efficiency, enhanced productivity, time-saving, greater flexibility, improved traceability, and decreased non-productive tasks)	BPS_1	0.820
– Market opportunities (e.g., more export markets)	BPS_2	0.805
– To meet increased demand	BPS_3	0.879
– Customers’ requirements	BPS_4	0.759
– Quality consistency	BPS_5	0.837
– International competition	BPS_6	0.740
Industry 4.0 HRD Preparedness	HRD		0.941	0.942	0.953	0.773
– The firm invests in employee training and development programs for Industry 4.0 technologies and skills.	HRD_1	0.856
– The firm ensures that employees possess the digital skills and knowledge necessary to support the adoption of Industry 4.0.	HRD_2	0.906
– The firm effectively evaluates the effectiveness of its HRD programs in terms of Industry 4.0 adoption.	HRD_3	0.879
– The firm provides adequate support to employees in adapting to Industry 4.0 technologies and processes.	HRD_4	0.896
– The firm effectively involves employees in the Industry 4.0 adoption process and ensures their buy-in.	HRD_5	0.888
– The firm faces significant challenges in building human resource capability for Industry 4.0 adoption.	HRD_6	0.848
Leadership Support for Industry 4.0	LDS		0.932	0.932	0.948	0.786
– Top executives fully comprehend the fundamentals of Industry 4.0.	LDS_1	0.872
– Top executives communicate regularly about Industry 4.0 through formal channels and official records.	LDS_2	0.907
– Top executives drive the formulation of Industry 4.0 policies and design an operational plan with a comprehensive roadmap.	LDS_3	0.906
– Top executives endorse the budget needed for Industry 4.0 implementations.	LDS_4	0.877
– Top executives support HRD initiatives for the adoption of Industry 4.0.	LDS_5	0.870
Technological Readiness for Industry 4.0	TRI		0.823	0.827	0.876	0.585
– Digital twin/VR/AR/MR	TRI_1	0.756
– Enhancement of energy efficiency	TRI_2	0.779
– Enhancement of logistics and manufacturing workflows	TRI_3	0.790
– Creation of transparency across the production process	TRI_4	0.756
– Automatic production control	TRI_5	0.741

α = Cronbach’s alpha; rho_A = reliability coefficient rho; CR = composite reliability; AVE = average variance extracted.

, which includes constructs related to Industry 4.0 adoption: business pressures (BPS), Industry 4.0 HRD preparedness (HRD), leadership support for Industry 4.0 (LDS), and technological readiness for Industry 4.0 (TRI). The indicator loadings for the items across these constructs exceed the recommended threshold, confirming strong indicator reliability.

The assessment of internal consistency reliability employed Cronbach’s alpha (α), composite reliability (CR), and the reliability coefficient rho (rho_A). The constructs under analysis exhibited values for α, CR, and rho_A within the recommended range of 0.7 to 0.95. This indicates a high level of internal consistency while avoiding redundancy [97]. Reliability values exceeding 0.95 may indicate redundancy among indicators and suggest undesirable response patterns [98]. Additionally, Dijkstra and Henseler (2015) mentioned that the value of rho_A should lie between CR and α [99]. As presented in Table 10, BPS shows reliability values of α = 0.894, CR = 0.918, and rho_A = 0.906; HRD displays values of α = 0.941, CR = 0.953, and rho_A = 0.942; LDS has α = 0.932, CR = 0.948, and rho_A = 0.932; and TRI records α = 0.823, CR = 0.876, and rho_A = 0.827. These results affirm that the indicators measuring the same construct are highly correlated, supporting the reliability of the constructs within the model. The findings contribute to a robust evaluation of the measurement model, demonstrating strong reliability and validity of all constructs [98,99].

The average variance extracted (AVE) metric was used to evaluate convergent validity, measuring the extent to which each construct explains variance in its associated indicators. This method provides a robust approach to ensure that the constructs are accurately represented by their respective items. A minimum value of 0.5 is deemed acceptable [100]. AVE values shown in Table 10 were all above the threshold of 0.50. As a result, a comprehensive assessment ensured the reliability and validity of the items and constructs.

In the next step, discriminant validity was verified through the Fornell–Larcker criterion and the Heterotrait/Monotrait Ratio of Correlations (HTMT).

Table 11. Discriminant validity: Fornell–Larcker analysis.

	BPS	HRD	LDS	TRI
BPS	0.808
HRD	0.481	0.879
LDS	0.583	0.601	0.886
TRI	0.238	0.408	0.427	0.765

demonstrates that the square root of each construct’s AVE surpasses its inter-construct correlations within the structural model, supporting discriminant validity [101]. Furthermore, all HTMT values in

Table 12. HTMT ratios for discriminant validity assessment.

	BPS	HRD	LDS	TRI
BPS
HRD	0.513
LDS	0.628	0.640
TRI	0.281	0.461	0.480

are below the 0.85 threshold [102], further supporting the validity of the discriminant construct.

4.2.4. Evaluation of Structural Relationships

Figure 3 presents the summarized results of the PLS-SEM analysis, detailing the path coefficients, significance levels, and coefficient of determination (R²) for each dependent variable. The structural model accounts for 22.1% of the variance in TRI, 38.7% in HRD, and 34.0% in LDS, indicating that the model demonstrates moderate to substantial explanatory power across the endogenous constructs [103]. To further evaluate the strength of these relationships, an effect size (f²) analysis was performed. This analysis quantifies the extent to which each exogenous construct contributes to the R² of an endogenous construct. According to thresholds established by [90], f² values of 0.02, 0.15, and 0.35 are interpreted as small, medium, and large, respectively.

The findings indicate that BPS has a significant effect on LDS (f² = 0.516), indicating a strong influence. Conversely, its influence on HRD is small (f² = 0.042), and its effect on TRI is negligible (f² = 0.003). LDS has a medium effect on HRD (f² = 0.254). Moreover, there is a small effect on TRI (f² = 0.067), suggesting it plays a moderate role in shaping these outcomes. Furthermore, HRD contributes a small effect to TRI (f² = 0.049). These effect size values illustrate the varying degrees of influence that predictor constructs exert within the model, with some relationships showing meaningful contributions and others exerting minimal impact.

In addition to explanatory power, the model’s predictive relevance was assessed using the blindfolding technique. All endogenous constructs exhibited Q² values greater than zero, confirming that the model possesses sufficient out-of-sample predictive capability. Taken together, the R², f², and Q² metrics demonstrate the robustness of the structural model in both explaining and predicting key outcome variables.

The model fit was examined by calculating the standardized root mean square residual (SRMR), which evaluates the divergence between the sample and model covariance matrices. Ref. [91] suggested that an SRMR value of 0.08 is appropriate for PLS path models. Thus, this study’s SRMR value of 0.060 indicates that the model fit criterion is satisfied.

Table 13. Summary of hypothesis testing results using PLS-SEM.

	Hypothesis	β	p-Values	Assumption
H1a	BPS > HRD	0.197 *	0.026	Supported
H1b	BPS > LDS	0.583 **	0.000	Supported
H1c	BPS > TRI	−0.066	0.482	Not Supported
H2a	LDS > HRD	0.486 **	0.000	Supported
H2b	LDS > TRI	0.314 **	0.000	Supported
H3	HRD > TRI	0.250 *	0.002	Supported

* p < 0.05, ** p < 0.01, β = path coefficients.

presents the hypotheses tested and their corresponding significance levels. The results show that the path from BPS to HRD is positive and significant (β = 0.197, p = 0.026), indicating that higher business pressures are associated with increased HRD preparedness for Industry 4.0. Additionally, the relationship between BPS and LDS is strongly positive and highly significant (β = 0.583, p < 0.001), indicating that greater business pressures are associated with stronger leadership support for Industry 4.0 initiatives. BPS was found to have insufficient evidence to support TRI (β = −0.066, p = 0.482), indicating that business pressures do not have a direct impact on technological readiness for Industry 4.0. LDS significantly influences HRD (β = 0.486, p = 0.000), demonstrating that strong leadership support enhances Industry 4.0 HRD preparedness. Both LDS (β = 0.314, p = 0.000) and HRD (β = 0.250, p = 0.002) positively affect TRI. This implies the critical role of leadership in enhancing the technological readiness of Industry 4.0. In addition, the results suggest that better-prepared HRD contributes to greater technological readiness for Industry 4.0 adoption.

PLS analysis involves the indirect effect of path coefficients. BPS has a total indirect effect on TRI (β = 0.304, p < 0.001) and HRD (β = 0.284, p < 0.001). BPS has been found to have a specific indirect effect on HRD (BPS > LDS > HRD: β = 0.284, p = 0.000). BPS also has a significant specific indirect effect on TRI, which is mediated by LDS (BPS > LDS > TRI: β = 0.183, p = 0.001), and both LDS and HRD (BPS > LDS > HRD > TRI: β = 0.071, p = 0.018). However, a significant specific indirect effect of BPS on TRI, which is mediated by HRD (BPS > HRD > TRI: β = 0.049, p = 0.088), is not significant. LDS was found to have an indirect significant effect on TRI (β = 0.122, p = 0.011). Its effects on TRI are shown to be mediated by HRD (LDS > HRD > TRI: β = 0.122, p = 0.011).

4.3. Comparison of Qualitative and Quantitative Results

The research employed a mixed-method approach to evaluate Industry 4.0 readiness and examine the impact of strategic human resource development (HRD). The combined results of qualitative and quantitative techniques offer a comprehensive perspective on the factors influencing successful adoption.

4.3.1. Summary of Qualitative Findings

The qualitative analysis, based on case studies of five companies, revealed significant differences in their readiness for Industry 4.0. Larger international companies (Companies 4 and 5) demonstrated high readiness levels, supported by strong leadership, well-defined strategies, and substantial investments in technology and HRD. These companies are at Level 5 of Industry 4.0 readiness. In contrast, smaller companies (Companies 1, 2, and 3) faced challenges such as budget constraints, lack of strategic planning, and inadequate training programs, resulting in lower readiness levels and highlighting the need for enhanced HRD strategies.

4.3.2. Summary of Quantitative Findings

Using PLS-SEM, the quantitative analysis confirmed the significance of business pressures, HRD preparedness, and leadership support in driving technological readiness. The results showed that business pressures have a positive influence on HRD preparedness (β = 0.197, p = 0.026) and leadership support (β = 0.583, p < 0.001), both of which are crucial for achieving technological readiness (β = 0.314, p < 0.001). However, the analysis found that business pressures do not directly impact technological readiness (β = −0.066, p = 0.482), indicating that the effect is mediated by leadership and HRD practices.

4.3.3. Integrative Analysis of Mixed-Method Findings

Qualitative and quantitative findings consistently emphasize the critical role of strategic HRD, leadership, and technological readiness in achieving Industry 4.0 readiness. Companies with higher readiness levels (Companies 4 and 5) benefited from comprehensive strategies, strong leadership, and robust HRD programs, which enabled the effective adoption of Industry 4.0 technologies. Conversely, companies with lower readiness (Companies 1, 2, and 3) needed more resources and planning, underscoring the need for targeted strategic HRD.

The integration of these findings highlights the importance of a holistic approach to Industry 4.0 readiness, where strategic HRD and leadership play crucial roles in navigating the challenges of digital transformation and ensuring companies are well-equipped to leverage new technologies.

5. Discussion

5.1. Strategic HRD as a Catalyst for Industry 4.0 Readiness

Both qualitative case studies and quantitative analysis highlight the critical role of strategic HRD in driving Industry 4.0 readiness. Firms that emphasize continuous learning, upskilling, and innovation-oriented HR practices demonstrate greater preparedness for adopting Industry 4.0 technologies. This is evidenced by the significant positive relationship between HRD preparedness and technological readiness (β = 0.250, p = 0.002). These findings are consistent with global research underscoring the need for internal digital competencies [63] and automation-led transformation [57]. Empirical work by [85] confirms that technologies like collaborative robots enhance HR performance by improving the integration of capabilities and workforce coordination. Strategic HRD thus plays a vital role in fostering dynamic capabilities essential for navigating digital transformation [104]. The PLS-SEM results validate that leadership support significantly strengthens the development of human capabilities, surpassing the influence of external environmental pressures. This finding underscores the critical role of internal strategic commitment in driving sustainable digital transformation. Consequently, for entrepreneurs and executives, investing in forward-looking strategic HRD that emphasizes digital skills, innovation, and adaptability is essential for the effective and sustainable implementation of Industry 4.0. As [6] demonstrates, strategic HRD initiatives such as digital HRM, continuous learning, and proactive management support enhance employee engagement and organizational performance, thereby contributing to long-term economic resilience. Complementing this, Ref. [2] highlights the importance of labor upskilling and human–machine collaboration in advancing socially sustainable and future-ready industries across emerging economies.

5.2. Leadership Drivers of Digital Transformation

The study’s findings highlight leadership as key in driving Industry 4.0 readiness. The qualitative analysis revealed that companies with strong leadership support were more likely to have robust HRD strategies and greater technological readiness. Quantitatively, leadership support was found to have a significant positive effect on HRD preparedness (β = 0.486, p < 0.001) and technological readiness (β = 0.314, p < 0.001). These results highlight the crucial role of leadership in not only allocating resources but also in cultivating an organizational culture that promotes continuous learning and innovation, aligning with the findings of [70], as well as [105]. Both studies highlight that effective leadership is crucial for the successful adoption and implementation of Industry 4.0 technologies. Leaders play a crucial role in driving digital transformation by acquiring the necessary digital competencies, guiding organizational change, and fostering an environment that promotes technological advancement. Ref. [70] mentioned that reskilling and upskilling top management to enhance digital leadership capabilities is important.

Entrepreneurs and top management with digital leadership can strengthen, navigate, and drive organizational changes in the digital era. The research by [105] found that effective leadership is crucial for guiding technological adoption, promoting a culture of continuous improvement, and ensuring the alignment of digital initiatives with organizational objectives. Additionally, this research supports the study of [68]. Leaders are the key people for innovation processes, especially for SMEs. The study by [6] found that digital human resource management significantly enhances employee engagement and organizational performance, with sustainable HR practices partially mediating this relationship.

Additionally, management support was found to moderate the effect of strategic HRD on engagement outcomes, suggesting that leadership involvement is crucial in translating digital HR initiatives into Industry 4.0 readiness. Another study by [106] demonstrates that transformational leadership significantly enhances digital transformation efforts, which in turn positively influences sustainable development outcomes in the public sector. This supports the view that leadership acts as a key enabler in aligning technological innovation with sustainability goals, an essential dynamic in driving the transformation to Industry 4.0. Thus, the results of this study suggest a broader theoretical framework in which leadership serves as both an enabler and a constraint in digital transformation. Integrating behavioral leadership theories with digital maturity models can provide deeper insights into the evolving role of leaders in managing the complexities of Industry 4.0. Leadership support for Industry 4.0 should prioritize consistent and clear communication about Industry 4.0 initiatives through formal channels and documented official records. A well-defined roadmap for Industry 4.0 policies, along with the development of a comprehensive operational plan, is also essential to guide successful implementation and ensure alignment with organizational goals. Also, top management should allocate adequate budgets to support Industry 4.0 initiatives, ensuring the availability of necessary financial resources for successful implementation. Furthermore, proactive leadership in promoting and investing in strategic HRD is crucial for building the competencies necessary for effective Industry 4.0 adoption and long-term organizational competitiveness.

5.3. Business Environment as a Driver of HRD and Technology Adoption

Business pressures were identified as significant drivers of HRD strategies and leadership support, as evidenced by the qualitative case studies and the quantitative findings. The quantitative analysis revealed a strong positive relationship between business pressures and leadership support (β = 0.583, p = 0.000) and a moderate relationship with HRD preparedness (β = 0.197, p = 0.026). However, business pressures did not directly impact technological readiness (β = −0.066, p = 0.482), suggesting that their effect is mediated by leadership and human resource development (HRD) practices. This finding is consistent with the work of [74], who emphasize the indirect nature of business pressures on technological adoption. In addition, the current study supports the research of [107], which suggests that product quality supported by advanced technology is crucial for building customer trust. This drives companies to shift technological levels for innovation. Business pressures, such as global competition, prompt SMEs to continually enhance their technological capabilities and workforce expertise [107]. Due to the increasing business pressure for operational efficiency and technological integration, HRD is highlighted as essential for training employees to effectively use new technologies [74,75]. Thus, organizations need to prepare HRD to ensure that global talent is effectively supported by the proper training and technological infrastructure [76]. The results of this study highlight that business pressures driving organizational change, particularly in SMEs, include the need to meet increased demand, ensure quality consistency, and achieve operational benefits such as improved productivity, cost efficiency, and flexibility. To address these pressures, organizations must focus on developing a workforce with the digital skills and knowledge necessary for adopting Industry 4.0 technologies. HRD efforts should prioritize training, support for adaptation, and employee involvement to ensure the successful integration of new technologies, thereby enhancing both organizational readiness and competitive advantage in response to these external challenges.

5.4. Synergistic Effects of Technological and Human Readiness

The interplay between technological readiness and human resource development (HRD) was a key focus of this research. The qualitative findings demonstrated that companies with well-prepared HRD strategies were more successful in adopting advanced Industry 4.0 technologies. This was supported by the quantitative analysis, which confirmed a significant positive relationship between HRD preparedness and technological readiness (β = 0.250, p = 0.002). These findings are consistent with [79], who underscore the pivotal role of HRD in aligning workforce capabilities with technological advancements. Workforce readiness to adopt emerging technologies is identified as a critical factor in overcoming technological challenges. HRD contributes significantly to enhancing technological readiness by developing the skills necessary for successful research and development activities [107]. To achieve organizational alignment, HRD strategies must be closely integrated with technological objectives to improve adaptability and sustain competitive advantage within Industry 4.0 [79].

Furthermore, the study by [6] reveals that external environmental factors such as regulatory pressures and industry competition positively impact the adoption of digital HRM systems, which subsequently promote sustainable HR practices. These practices, including unbiased recruitment, equitable reward systems, and tailored employee development, partially mediate the relationship between technological adoption and employee engagement. Collectively, these insights highlight that strategic HRD not only facilitates technological readiness but also embeds sustainability within organizational processes and workforce development. Based on the study’s findings, organizations should develop an HRD roadmap for Industry 4.0 adoption that begins with building employees’ digital skills and knowledge to support technological readiness. Following this, firms must provide continuous support to help employees adapt to Industry 4.0 technologies, ensuring they feel confident and capable. Lastly, it is essential to actively involve employees in the adoption process, securing their buy-in through clear communication and participation, which fosters a sense of ownership and commitment to digital transformation. This approach ensures a smoother transition and maximizes the potential of Industry 4.0 technologies.

5.5. Cross-Case Analysis: Firm-Level Differences in Industry 4.0 Readiness

The cross-case analysis revealed significant variability in Industry 4.0 readiness across different companies. Larger international companies were found to be more advanced in readiness, supported by comprehensive human resource development (HRD) strategies and strong leadership. In contrast, smaller companies faced challenges such as budget constraints and inadequate training programs, resulting in lower readiness levels. The quantitative findings support these qualitative insights, highlighting the importance of leadership and HRD in bridging the gap between business pressures and technological readiness. These findings are consistent with those of [58], who discuss the variability in Industry 4.0 readiness across different industries and regions.

5.6. Future Research Directions

This research offers practical guidance for policymakers and industry leaders in Thai industries, suggesting that a strategic HRD framework, supported by strong leadership, is essential for navigating the challenges of Industry 4.0. The findings also contribute to the academic discourse by linking technological readiness with human resource development (HRD) and leadership, highlighting the importance of an integrated approach to HRD.

The integrated findings from qualitative and quantitative analyses provide practical insights for enhancing Industry 4.0 readiness in the Thai manufacturing sector. Organizations should focus on developing strategic HRD frameworks and strengthening leadership support to navigate the challenges of digital transformation. Future research could explore the role of organizational culture and employee perceptions in shaping HRD and technological readiness, offering a more comprehensive understanding of Industry 4.0 adoption. Additionally, investigating how smaller enterprises can effectively implement strategic HRD practices in resource-constrained environments would provide further insights into the adoption of Industry 4.0 across diverse contexts.

6. Conclusions

Industry 4.0 technologies, including cyber–physical systems (CPS), robotics, artificial intelligence (AI), and the Internet of Things (IoT), have profoundly transformed the manufacturing industry. However, many Thai industries need help in aligning their workforce with the demands of these new technologies. Traditional human resource development (HRD) practices often need to improve in equipping employees with the necessary skills and adaptability required for Industry 4.0. This research addresses this gap by proposing a strategic HRD framework to enhance Industry 4.0 readiness within Thai industries.

To explore Industry 4.0 readiness and the role of strategic human resource development (HRD), this research employed a mixed-method approach, combining qualitative case studies from five Thai manufacturing companies with quantitative analysis of survey data from 144 firms. The qualitative data provided deep insights into the specific HRD challenges and strategies within the context of Industry 4.0. At the same time, the quantitative analysis, conducted using PLS-SEM, tested the relationships between business pressures, leadership support, HRD preparedness, and technological readiness.

Leadership emerged as the most critical factor influencing Industry 4.0 readiness, as evident in both qualitative and quantitative findings. Companies with strong leadership were more likely to develop robust HRD strategies, align their workforce with technological advancements, and successfully integrate Industry 4.0 technologies. Additionally, the study highlighted the role of external business pressures in shaping strategic HRD practices and the adoption of Industry 4.0.

The findings underscore the essential role of strategic HRD in facilitating Industry 4.0 readiness. Effective HRD practices, including continuous learning, upskilling, and fostering a culture of innovation, are crucial for organizations to adapt to the rapidly evolving technological landscape. Companies that proactively invest in their workforce and embrace technological advancements are better positioned to capitalize on the benefits of Industry 4.0 and maintain a competitive edge.

The qualitative analysis revealed significant disparities in Industry 4.0 readiness between large international firms and smaller organizations. Larger companies demonstrated advanced readiness, driven by well-defined strategic roadmaps, extensive technology investments, and comprehensive employee training programs. In contrast, smaller firms struggled with budget constraints, limited strategic planning, and inadequate HRD initiatives, resulting in lower readiness levels.

The integration of qualitative and quantitative findings highlights the importance of strong leadership and strategic human resource development (HRD) for the successful adoption of Industry 4.0. Organizations must prioritize strategic HRD to build a workforce capable of adapting to Industry 4.0 technologies. This includes implementing continuous learning programs, upskilling employees in new technologies, and fostering a culture of innovation and agility. Companies that invest in comprehensive HRD initiatives are better equipped to navigate the challenges of Industry 4.0 and maintain a competitive edge.

Strong leadership support is crucial for the successful adoption of Industry 4.0. Leaders should champion and invest in integrating advanced technologies, ensuring their organizations are technologically prepared and aligned with their strategic vision for Industry 4.0. Leadership must also guide the organization through the transition, providing clear direction and support to employees as they adopt the new system.

For companies facing budgetary constraints, it is essential to strategically allocate resources to areas that will yield the highest return on investment for Industry 4.0 readiness. This may involve phased technology adoption, targeted HRD initiatives, or partnerships with external stakeholders to share the costs of technological upgrades and training. Developing customized Industry 4.0 roadmaps that reflect unique challenges, opportunities, and resource constraints will allow organizations to systematically enhance their readiness while managing risks and uncertainties associated with new technology adoption.

Moreover, collaboration between departments and external partners can facilitate smoother transitions to Industry 4.0. Sharing knowledge and best practices within and across organizations can help mitigate the challenges associated with adopting new technologies and processes, ensuring that all parts of the organization are aligned and ready for the transition. These practical steps can help organizations achieve Industry 4.0 readiness and position themselves as leaders in their respective industries by fully leveraging the benefits of this industrial revolution.

This study contributes to the existing body of knowledge by empirically identifying the key enablers of Industry 4.0 readiness, emphasizing the pivotal role of dynamic capabilities and strategic HRD in fostering sustainable transformation. By demonstrating how internal competencies, particularly leadership support and workforce development, drive successful digital adoption, the findings offer practical guidance for manufacturing enterprises aiming to align technological innovation with long-term economic and social sustainability. In doing so, the study underscores the strategic integration of HRD and dynamic capability theory as foundational to building resilient, future-ready organizations in the Industry 4.0 era.

While the findings offer valuable insights, several limitations should be acknowledged. In the qualitative phase, the sample was limited to selected manufacturing firms within a specific regional context, which may affect the generalizability of the results. Additionally, the absence of formal coding techniques and reliance on cross-case comparison may have introduced interpretive bias, despite efforts to ensure consistency through structured analysis. In the quantitative phase, data were collected at a single point in time, which restricts the ability to infer causal relationships. Furthermore, the use of self-reported survey data may be subject to response bias. Despite these limitations, the integration of both methods provides complementary insights into Industry 4.0 readiness and strategic HRD in emerging economies.

Subsequent studies could investigate how organizational culture influences Industry 4.0 readiness, with particular attention to the role of cultural factors in facilitating or hindering the adoption of new technologies and processes. Additionally, examining the perceptions and attitudes of employees toward Industry 4.0 initiatives could provide a more holistic view of the challenges and opportunities associated with its implementation. A comprehensive understanding of employee concerns and motivations is crucial for developing effective change management strategies, thereby enhancing adoption rates and contributing to the long-term growth and development of the organization.