The Impact of Industry 4.0 Practices on Sustainable Performance in Jordan’s Retail Sector: The Moderating Role of Environmental Dynamism

Abstract

Background: The retail sector in Jordan is adopting Industry 4.0 (I4.0) technologies to enhance efficiency and sustainability. Nevertheless, there is a lack of empirical evidence to inform retail managers regarding the impact of I4.0 adoption on environmental, economic, and social sustainability, particularly in dynamic contexts. Therefore, this study aims to investigate the impact of Industry 4.0 on the three types of sustainable performance, with the moderating effect of environmental dynamism. Methods: This quantitative study collected data using a cross-sectional survey of 100 retail professionals from large companies that was analyzed using structural equation modeling (SEM) to test the hypotheses. Results: I4.0 practices improved retail environmental, economic, and social sustainability. Additionally, environmental dynamism moderated the relationship between I4.0 and environmental sustainability, suggesting that dynamic environments may reduce the effectiveness of I4.0 technologies in driving environmental performance. Economic and social sustainability had no significant moderating effects. Conclusions: This study examines the sustainability benefits of I4.0 adoption in an unexplored developing economy. It emphasizes the strategic importance of digital transformation for retail sustainability and provides practical recommendations for dynamic markets. The findings support I4.0 technologies role in sustainable growth and lay the groundwork for digital transformation research in emerging markets.

1. Introduction

1.1. Background and Research Motivation

The global industrial landscape is undergoing a significant transformation with the emergence of Industry 4.0 (I4.0) which is a technological paradigm that integrates digital innovations such as cyber-physical systems (CPS), the Internet of Things (IoT), big data analytics (BDA), artificial intelligence (AI), and cloud computing (CC) to create interconnected, autonomous, and intelligent operational environments [1,2]. These advanced technologies enable real-time decision-making, predictive maintenance, and decentralized control, leading to enhanced productivity, agility, and resource efficiency [3,4]. Notably, I4.0 is also increasingly associated with sustainability gains, such as minimizing waste, lowering emissions, and promoting circularity in business operations [5,6].

While I4.0 originated in technologically advanced economies, its application has expanded to developing countries, where its implementation is shaped by contextual factors such as institutional infrastructure, technological readiness, and socio-economic constraints [7]. In this context, Jordan presents a particularly compelling case for investigation. As an emerging market with a rapidly growing retail sector, Jordan is striving to reconcile digital transformation with sustainable development goals [8,9]. The retail sector, which constitutes a significant portion of the Jordanian economy and workforce, offers a diverse mix of large retail chains, and e-commerce platforms that are increasingly pressured to modernize in response to global competition and shifting consumer expectations [10,11,12].

Compared to other developing countries, Jordan’s national policy environment that is characterized by initiatives such as the Green Growth Plan and Digital Economy Action Plan demonstrates a proactive commitment to integrating sustainability and digital innovation [13]. However, despite policy alignment, the practical implementation of I4.0 remains fragmented, largely due to structural challenges such as high capital costs, limited access to digital infrastructure, and a shortage of digital talent [14,15]. Furthermore, the country faces high levels of environmental dynamism (EVD), marked by economic fluctuations, regulatory volatility, and political instability in the broader Middle East region [16,17]. These characteristics position Jordan as an illustrative and high-need case for exploring the intersection of I4.0 adoption, sustainability, and environmental uncertainty in emerging retail markets.

1.2. Research Problem and Gap of the Study

Existing research on I4.0 has primarily centered around its application in manufacturing sectors within developed countries [18,19]. These studies generally assume stable environments and focus on efficiency and innovation outcomes, with relatively limited attention to sustainability impacts in volatile, resource-constrained markets [20,21]. While recent work acknowledges the potential of I4.0 to contribute to environmental, economic, and social sustainability [18,22,23], empirical evidence from the retail sector in emerging economies remains scarce and scattered [24,25]. This is despite the fact that retail is a critical engine for job creation, consumption, and digital diffusion in such contexts [10,26].

Retail’s digital transformation trajectory is also unique, often involving customer-facing technologies (e.g., AR/VR and AI recommender systems), last-mile logistics integration, and omnichannel business models [26,27,28]. These features diverge substantially from manufacturing-centric applications of I4.0 and, thus, require sector-specific examination [29,30]. However, few studies have explored how such technologies are being operationalized in retail firms in emerging markets like Jordan, or how they affect sustainability outcomes across environmental (ENS), economic (ECS), and social (SOS) dimensions.

Moreover, while EVD has been studied as a moderator in innovation and strategy contexts [31,32], its role in shaping the effectiveness of I4.0 in achieving sustainable performance remains underexplored—particularly in retail [33,34,35]. The high volatility characteristic of the MENA region makes this an urgent research priority. In Jordan, where firms are often reactive rather than strategic in their digital adoption [11,15,36], it is unclear how external uncertainty conditions the I4.0–sustainability relationship. This ambiguity limits both theoretical understanding and practical guidance.

Despite growing interest in the transformative potential of I4.0, empirical evidence on its application and impact in the retail sector of emerging markets remains limited. Specifically, Jordanian retailers face structural and strategic challenges that hinder effective digital transformation and the integration of sustainability goals [14,37]. The interplay between I4.0 adoption and EVD in shaping sustainable performance is insufficiently theorized and empirically tested [31,38]. There is, thus, a need for context-specific research that addresses this gap and provides actionable insights for firms operating under dynamic and uncertain conditions.

To address these challenges, the study is guided by the following research question:

To what extent does the adoption of I4.0 practices influence environmental, economic, and social sustainability in Jordan’s retail sector, and how does EVD moderate these relationships?

1.3. Research Contributions

This study makes four key contributions that collectively advance both scholarly understanding and managerial practice in the fields of I4.0 and sustainability in emerging market contexts.

First, this study offers a contextual contribution by providing novel empirical evidence from Jordan’s retail sector—an underrepresented area in the existing I4.0 and sustainability literature. Whereas prior research has predominantly focused on manufacturing industries in developed economies [18,19,24], this study emphasizes the realities of digital transformation in a resource-constrained, high-volatility emerging market. The Jordanian retail context—characterized by fragmented digital adoption, limited institutional support, and heightened environmental uncertainty—provides a unique empirical setting to examine how I4.0 technologies can be harnessed for sustainable development.

Second, the study advances theoretical understanding by integrating the creating shared value (CSV) framework with the resource-based view (RBV). Prior studies often employ a single theoretical lens to examine I4.0 adoption [20,25], thereby overlooking the complex interaction between internal capabilities and external value creation. By combining CSV and the RBV, this research conceptualizes how digital capabilities serve not only as internal strategic assets but also as mechanisms for delivering economic, social, and environmental value to stakeholders. This dual-theory approach offers a more comprehensive explanation of how firms can achieve both competitiveness and sustainability through digital innovation.

Third, the study contributes methodologically by introducing and empirically testing EVD as a moderating variable in the relationship between I4.0 adoption and sustainability performance. Although EVD has been considered in broader innovation and strategy research [31,32], its role in moderating the digital transformation–sustainability nexus—especially in the retail context of emerging markets—remains largely unexamined. By capturing the interaction between I4.0 and EVD, this study provides a more realistic and nuanced model of how firms can adapt digital strategies to volatile and unpredictable external conditions [33,35,38].

Fourth, this study offers a practical contribution by generating actionable insights for managers and policymakers operating in uncertain, rapidly changing environments. Specifically, it helps retail managers understand which I4.0 technologies are most effective in enhancing environmental, economic, and social sustainability under varying levels of external dynamism. This allows firms to tailor their digital investments and capability-building efforts accordingly. Moreover, the findings provide evidence-based recommendations for national policy initiatives by identifying key infrastructural and institutional barriers to effective I4.0 deployment in Jordan. Unlike general prescriptions offered by prior research [14,15], this study delivers context-sensitive guidance that can inform both organizational strategies and public policy in the MENA region and other emerging markets facing similar challenges.

The remainder of this paper is organized as follows: Section 2 reviews the relevant literature and presents the theoretical foundation, conceptual framework, and hypotheses. Section 3 outlines the research methodology, including sampling, data collection, and measurement instruments. Section 4 presents the data analysis and results using structural equation modeling (SEM). Section 5 discusses the findings in light of prior research and contextual implications. Section 6 concludes the study by highlighting theoretical and managerial contributions, identifying limitations, and proposing directions for future research.

2. Literature Review

2.1. Theoretical Framework

The adoption of I4.0 technologies represents not only a means for operational enhancement but also a strategic avenue for delivering long-term, sustainable value. To understand this dual potential, this study draws upon two complementary theoretical lenses: CSV and the RBV. When applied together, these theories offer a comprehensive explanation of how I4.0 technologies can drive environmental (ENS), economic (ECS), and social sustainability (SOS) through both external value creation and internal resource configuration.

The concept of CSV, introduced by Porter and Kramer [39], argues that companies can enhance their competitiveness by addressing societal and environmental challenges in ways that also generate economic returns. CSV positions sustainability not as a trade-off, but as a business opportunity: Firms can achieve operational efficiencies, unlock new markets, and foster stakeholder trust by aligning profit objectives with positive societal impact [39,40]. In the I4.0 context, this implies deploying technologies such as AI, BDA, and IoT to reduce emissions, improve working conditions, or serve underserved communities—thus achieving the dual goals of profitability and social responsibility [24,29]. This is especially relevant in emerging markets like Jordan, where retail firms operate under increasing scrutiny to contribute to social development while maintaining commercial viability amid institutional and environmental constraints [10,11].

While CSV outlines what firms should aim for—shared economic and societal value—the RBV provides insight into how firms can build the internal capabilities required to achieve those aims. The RBV, as articulated by Barney [41], asserts that sustainable competitive advantage stems from developing and leveraging resources that are valuable, rare, inimitable, and organizationally embedded (VRIO). Within the digital transformation landscape, I4.0 tools—such as cloud infrastructure, predictive analytics, and IoT ecosystems—only become strategic assets when they are customized to a firm’s needs, embedded into core operations, and difficult for rivals to replicate [7,24]. For example, a Jordanian retail firm that integrates AI-driven customer analytics and IoT-enabled inventory management into its workflows may gain differentiated value through enhanced ECS and service innovation [28,29].

More importantly, CSV and the RBV are not standalone theories in this study—they operate in synergy. CSV defines the strategic direction of aligning I4.0 use with societal benefit (ENS and SOS), while the RBV explains the organizational processes needed to acquire, configure, and apply I4.0 technologies effectively. The integration of these two frameworks highlights that achieving sustainability through I4.0 depends not merely on adoption but also on strategic deployment—where firms use their internal capabilities to meet external societal needs. This connection is crucial in volatile markets like Jordan, where external uncertainty (e.g., regulatory shifts, demand fluctuations) amplifies the need for firms to develop adaptive, capability-based responses [4,18,23,42].

In this regard, the RBV contributes to understanding how I4.0 capabilities can be embedded and mobilized, while CSV ensures that these capabilities are purposefully directed toward creating shared economic, environmental, and social value. For instance, using BDA to optimize logistics routes (an RBV capability) not only reduces operational costs (ECS) but also lowers carbon emissions (ENS), which is a CSV-aligned outcome [14,20]. Similarly, digital upskilling through AI-driven platforms can enhance workforce productivity (RBV) and promote social equity (CSV) [14,22].

Applied to the Jordanian retail sector, the CSV–RBV integration provides a strong theoretical foundation for examining how I4.0 technologies contribute to triple bottom line sustainability under conditions of EVD. It also informs the hypothesis development by proposing that firms with well-developed digital capabilities (RBV) and shared value orientation (CSV) are better positioned to align I4.0 adoption with sustainability outcomes, particularly when navigating uncertain and resource-constrained environments [13,38,43].

2.2. Industry 4.0 Practices

I4.0 represents a transformative shift in industrial and service operations, characterized by the integration of digital technologies such as the Internet of Things (IoT), big data analytics (BDA), cloud computing (CC), and artificial intelligence (AI) [7,24]. Often defined as “the present trend in automation and data exchange in organizations” [44,45], I4.0 enables intelligent, autonomous, and interconnected systems that optimize decision-making, resource use, and operational efficiency. Although I4.0 initially emerged in the manufacturing sector, its relevance has grown rapidly in service-oriented industries such as retail, particularly in developing countries like Jordan, where it offers new avenues for enhancing agility, competitiveness, and sustainability [10,26].

I4.0 practices are typically classified into three core functional domains: automation, analytics, and connectivity [5,46]. Each domain holds unique implications for retail operations, which differ significantly from manufacturing in structure, customer interface, and service orientation. For example, while automation in manufacturing often refers to robotic assembly lines, retail automation focuses more on technologies such as self-checkout kiosks, automated inventory systems, and digital shelf monitoring—tools aimed at improving customer experience and operational precision [28,45].

However, the adoption of such automation tools is not without challenges. In developing countries, automation often leads to job displacement among low-skilled retail workers, widening the digital divide and intensifying workforce polarization [45]. Studies from European production contexts highlight that while automation can raise productivity, it also shifts labor demands toward high-skilled workers—an adjustment that may not be feasible in markets with low levels of digital literacy or inadequate vocational training infrastructure [47]. In Jordan, many retail firms lack the human capital and financial capacity to absorb these shifts, making inclusive digital transformation difficult to achieve [7,14,27].

Analytics, enabled by BDA and AI, has the potential to generate real-time insights into customer preferences, market trends, and inventory turnover. Retailers can leverage these insights to improve demand forecasting, personalize offerings, and optimize pricing strategies, thereby enhancing economic sustainability (ECS) and customer satisfaction [29,48]. Yet, retail-specific barriers such as fragmented legacy systems, limited data integration across channels, and inconsistent data quality often undermine the effectiveness of analytics tools—especially in emerging economies [11,23]. This reflects a key limitation in extrapolating manufacturing-based findings to retail, where the customer-facing, fast-paced, and omnichannel nature of operations presents different implementation challenges.

Connectivity, driven by IoT and CC, supports real-time synchronization across devices, platforms, and processes, improving supply chain transparency, energy use monitoring, and service responsiveness [38,49]. In the retail context, this may include IoT-enabled smart shelves, RFID for product tracking, and cloud-based CRM systems. These tools are central to advancing environmental sustainability (ENS) through energy optimization and reduced waste. However, increased connectivity also introduces vulnerabilities—especially in regions with underdeveloped digital infrastructure or weak cybersecurity governance. As Sony [29] notes, highly interconnected systems are particularly prone to cyberattacks, and in the absence of robust data protection frameworks—as is often the case in developing countries—retailers risk reputational damage, data loss, and regulatory penalties.

From the perspective of the RBV, I4.0 technologies can serve as strategic resources only when they are valuable, rare, inimitable, and organizationally embedded (VRIO) [41,50]. This means that merely acquiring digital tools is insufficient; success depends on the firm’s ability to strategically adapt and integrate these technologies into core processes. In retail, this often requires significant changes in organizational routines, employee training, and customer engagement models [24,42]. Unfortunately, many retail firms in developing economies struggle to internalize these capabilities due to fragmented organizational structures and limited change management expertise [7,27].

Complementarily, the CSV approach views I4.0 as a platform for pursuing both economic and societal goals [45]. In retail, this could involve using AI to optimize logistics for lower emissions, deploying digital channels to serve remote or underserved populations, or introducing eco-efficient store operations [29,40]. Such dual value creation is especially important in Jordan’s retail sector, where aligning firm performance with societal well-being is increasingly expected due to mounting socio-environmental pressures [10,11]. However, CSV’s potential is constrained when firms lack supportive institutional frameworks, policy incentives, or sector-specific guidelines for implementing socially responsible digital strategies [20,23].

In sum, while I4.0 holds a significant promise for transforming the retail sector, its adoption in developing economies remains uneven and fraught with context-specific limitations. Much of the existing literature remains manufacturing-centric and fails to fully account for the operational, organizational, and customer-centric nuances of retail. This study, therefore, emphasizes the need to examine I4.0 adoption through a retail-specific lens that accounts for its unique challenges, such as high customer interaction, omnichannel integration, and sensitivity to demand fluctuations—especially within dynamic environments like Jordan.

2.3. Industry 4.0 and Sustainability

Sustainability, within this study, means that the concept has multiple dimensions that encompass environmental stewardship, economic viability, and social equity. ENS means reducing the ecological footprint and preserving nature [51]. ECS means long-term profitability, optimization of resources, and innovation [52]. SOS means the health and well-being of employees, consumers, and the community, including ethical operations and fair results [53]. These three dimensions together form the basis for sustainable development within the retailing sector, most prominently in the fast-developing marketplace within Jordan. I4.0 technologies’ integration into operations holds the potential for the transformation of all three dimensions of sustainability.

2.3.1. Environmental Sustainability and Industry 4.0

Environmental sustainability (ENS) has become a critical strategic imperative for firms worldwide, driven by escalating concerns over climate change, carbon emissions, and resource scarcity [20]. In this context, I4.0 technologies offer transformative potential by enabling firms to optimize resource utilization, reduce environmental externalities, and transition toward low-impact business models. Technologies such as IoT, cyber-physical systems (CPS), AI, and smart sensors facilitate the real-time monitoring of energy use, emissions, and material flows—providing the tools necessary for data-driven environmental decision-making [19].

From a CSV perspective, I4.0 allows firms to simultaneously pursue profitability and environmental stewardship by embedding sustainability into their operational logic [39,40]. For instance, IoT-enabled systems can detect inefficiencies in energy consumption and reduce material waste through precision tracking, while AI-powered platforms can optimize transportation routes to reduce emissions. These practices not only minimize the environmental impact but also contribute to cost savings and enhanced brand reputation—core pillars of shared value creation.

Empirical evidence supports this theoretical argument. Oláh et al. [20], in their longitudinal review of the I4.0 literature, concluded that while some digital technologies may have initial energy demands, their integration into broader sustainability frameworks significantly improves environmental outcomes over time. Javaid et al. [19] similarly identified multiple applications of I4.0 that support eco-efficiency, such as smart manufacturing systems, automated quality control, and energy-aware robotics. These systems help firms achieve cleaner production goals and regulatory compliance.

Moreover, Bai et al. [9], using a multi-criteria decision-making approach aligned with the United Nations Sustainable Development Goals (SDGs), found that I4.0 adoption contributes to several environmental targets, though outcomes vary depending on sectoral and contextual factors. This suggests that firms must strategically align I4.0 investments with environmental priorities to unlock maximum value. In support, Lobo Mesquita et al. [46] highlighted the positive interaction between lean practices and I4.0 tools in driving ENS outcomes, demonstrating that the digital enablement of waste minimization processes strengthens environmental performance.

In the retail sector, these mechanisms are particularly relevant. Unlike manufacturing, retail operations involve extensive energy use across store locations, logistics networks, and warehousing. Technologies such as IoT-enabled energy management systems, AI-based inventory planning, and digital traceability tools offer targeted solutions to reduce overstocking, lower energy consumption, and minimize packaging waste [31,36]. For Jordanian retailers, who face growing regulatory and consumer pressure to adopt green practices amid infrastructure constraints, I4.0 provides a viable pathway to integrate environmental considerations into daily operations.

From a RBV standpoint, firms that successfully embed I4.0 capabilities into their routines and infrastructure gain valuable, rare, and hard-to-imitate resources that enhance sustainable competitiveness [41,50]. These digital assets, when configured to support green operations, can differentiate firms in markets where environmental compliance and consumer eco-consciousness are rising trends.

Therefore, based on theoretical reasoning and empirical support, it is expected that I4.0 adoption enhances environmental sustainability in Jordan’s retail sector. Consequently, the following hypothesis is proposed:

H1: 

The adoption of I4.0 practices positively influences ENS in the retail sector of Jordan.

2.3.2. Economic Sustainability and Industry 4.0

Economic sustainability (ECS) refers to a firm’s ability to ensure long-term profitability, resilience, and competitiveness by maximizing resource efficiency, controlling costs, and fostering innovation-driven growth [51]. In the context of digital transformation, I4.0 technologies such as AI-driven demand forecasting, automated inventory control, and real-time analytics are increasingly seen as enablers of these outcomes [25]. These technologies allow firms to streamline operations, reduce waste, and respond more rapidly to market fluctuations—key pillars of economic sustainability.

From a RBV perspective, these digital technologies can become strategic assets when they meet the VRIO criteria [41,50]. For example, AI-powered analytics that enhance sales forecasting or inventory turnover can give retailers a competitive advantage by improving stock management and reducing carrying costs. Johnson et al. [28] showed that data-driven tools significantly reduce overstocking and stockouts—two common retail inefficiencies that directly affect both profit margins and customer satisfaction.

The potential of I4.0 to support ECS is further reinforced by Villar et al. [29], who applied soft systems methodology to highlight how systemic digital integration promotes economic efficiency and sustainable growth. While their study focused on Scandinavian industries, the broader implication is that digital tools, when aligned with firm-wide strategies, can support sustainable development through increased operational agility and innovation. Similarly, Bag and Pretorius [25] emphasized that I4.0 technologies facilitate the application of circular economy principles, helping firms reduce costs by reusing resources and improving production flexibility.

From a dynamic capabilities perspective, firms can only translate I4.0 adoption into economic value if they possess the ability to sense emerging opportunities, seize them, and reconfigure internal resources accordingly [42]. Merely acquiring advanced technologies is insufficient. Firms must strategically integrate these tools into their workflows, decision-making processes, and value chains. This is particularly relevant in volatile environments like Jordan, where firms face not only market uncertainty but also infrastructure and institutional constraints [1].

However, the literature also cautions against assuming that I4.0 adoption automatically leads to positive ECS outcomes. Schwab [54] warned that automation and intelligent systems may disrupt labor markets, particularly in developing economies where employment is more vulnerable. Therefore, the realization of economic benefits from I4.0 depends heavily on organizational readiness, digital literacy, and leadership commitment. Shiralkar et al. [55] further argued that ECS depends not just on digital capability, but on the alignment of these tools with local market dynamics and long-term business strategies.

In Jordan’s retail sector, where operational inefficiencies, fragmented supply chains, and narrow profit margins are common challenges, the strategic implementation of I4.0 can support ECS by improving cost control, enhancing operational agility, and boosting competitiveness. AI-based sales prediction, for example, can help firms make more accurate procurement decisions; IoT-enabled monitoring can reduce utility costs; and cloud-based platforms can scale operations without large capital investments. Yet, these benefits are most likely to materialize when I4.0 adoption is supported by strong digital capabilities and strategic alignment.

Therefore, based on theoretical frameworks and empirical evidence, it is proposed that I4.0 practices positively influence economic sustainability in Jordan’s retail sector as follows:

H2: 

The adoption of I4.0 practices positively influences ECS in the retail sector of Jordan.

2.3.3. Social Sustainability and Industry 4.0

Social sustainability (SOS) emphasizes the long-term well-being, equity, and inclusion of employees, customers, communities, and broader society. As organizations undergo digital transformation, I4.0 technologies are increasingly recognized not only for their economic and operational benefits but also for their capacity to support inclusive and socially responsible business practices [53]. Technologies such as AI, IoT, and big data analytics enable firms to enhance customer engagement, personalize services, and improve workplace safety—all of which contribute to socially sustainable outcomes.

From a CSV perspective, firms generate shared benefits when digital innovations enhance societal well-being alongside profitability [39,40]. I4.0 technologies allow businesses to tailor services to diverse customer needs, improve access for underserved groups, and offer safer, more inclusive environments for workers. Wolniak and Grebski [43] assert that I4.0 supports accessibility and inclusivity through technology-enabled customization, aligning closely with the principles of universal design outlined by Stephanidis and Savidis [56]. For example, retailers using AI can develop personalized shopping experiences for individuals with disabilities or limited digital literacy, thus promoting social equity and inclusiveness in service delivery.

Furthermore, I4.0 enhances SOS by transforming traditional workplace structures. IoT-enabled smart workplaces, automation-assisted roles, and remote working technologies improve job quality and safety, enabling more flexible and resilient work environments [57]. Papetti et al. [57] showed that digitalized industrial processes contribute to improved psychological well-being, job satisfaction, and opportunities for personal development—outcomes aligned with global standards for decent work and occupational health. These benefits are especially valuable in emerging economies where workforce conditions often lag behind international benchmarks.

From a RBV standpoint, socially enabling technologies can become valuable and inimitable resources when embedded in firm culture and HR practices [41]. Firms that leverage I4.0 for workforce development, ethical labor practices, and employee well-being gain reputational and operational advantages that enhance long-term resilience. For instance, digital upskilling platforms and AI-supported learning systems not only improve firm productivity but also contribute to employee empowerment, engagement, and loyalty.

However, concerns remain regarding the potential social risks of I4.0 adoption. Automation may displace low- and mid-skilled workers, especially in developing economies where job opportunities are limited and social safety nets are weak [45]. Additionally, Madzik [58] warns that the widespread use of personal data in digital systems can lead to ethical dilemmas around surveillance, privacy, and algorithmic bias. These risks highlight the importance of strategic and ethically guided implementation of I4.0 technologies, ensuring that digital transformation does not come at the expense of equity and worker protection.

In the context of Jordan, these considerations are particularly salient. The retail sector employs a large and diverse labor force, many of whom are vulnerable to job insecurity and lack access to continuous training. Abaddi [59] found that the social acceptability of technological change is a key determinant of successful innovation in Jordan. As such, the potential of I4.0 to improve work conditions, support social inclusion, and build societal trust is especially relevant in this context. Firms that integrate social goals into their digital strategies are more likely to achieve sustainable transformation and public legitimacy.

In sum, both theory and empirical evidence suggest that I4.0 technologies—when deployed ethically and strategically—can enhance social sustainability by improving workplace conditions, enabling inclusive service delivery, and supporting workforce development. These contributions are critical for emerging economies such as Jordan, where social equity and labor market evolution are pressing policy and business concerns. Consequently, H3 can be depicted as follows:

H3: 

The adoption of I4.0 practices positively influences SOS in the retail sector of Jordan.

2.4. Environmental Dynamism

EVD refers to the extent of unpredictability, turbulence, and rapid change in a firm’s external environment [60]. It captures conditions arising from volatile markets, regulatory fluctuations, fast-paced technological developments, and socio-political instability forces. These usually complicate firms’ long-term planning and compel them to continuously adapt to these circumstances [31,61]. EVD is often conceptualized using related dimensions such as uncertainty (lack of future information), volatility (frequency of change), and complexity (the interdependence of environmental variables) [62,63]. Together, these conditions affect how firms sense, seize, and reconfigure resources in response to change.

EVD has been widely studied as a moderating factor in strategic management and innovation literature. Schilke [64] demonstrated that EVD alters the strength of the relationship between dynamic capabilities and firm performance, with an inverted U-shaped pattern suggesting that moderate levels of dynamism are most conducive to advantage creation. Chan et al. [33] found that EVD moderates the link between green innovation and firm performance, arguing that external instability heightens the strategic relevance of sustainable innovation. Similarly, Mura et al. [32] and Ensley et al. [65] observed that EVD shapes how leadership and social capital affect knowledge generation and organizational adaptability.

Despite its centrality in innovation studies, EVD’s role in modulating the relationship between I4.0 adoption and sustainability performance remains underexplored. I4.0 adoption is not solely a reaction to digital trends. It is increasingly a strategic response to dynamic external pressures, including shifting consumer expectations, stricter environmental regulations, and fluctuating supply chains [21]. In such environments, sustainability is no longer optional; rather, it becomes a competitive imperative. EVD, thus, shapes not only the urgency of I4.0 adoption but also its effectiveness in achieving environmental, economic, and social performance goals.

For instance, Farrukh Shahzad et al. [34] demonstrated that EVD positively moderates the relationship between I4.0-driven green supply chain collaboration and sustainable outcomes. This effect was especially evident in industries under high regulatory and market pressure. Similarly, Rehman et al. [35] found that in dynamic environments, digital technologies have a stronger impact on innovation capacity, which then translates into improved environmental performance. These findings underscore that EVD magnifies the strategic value of I4.0 when firms are forced to innovate, reduce inefficiencies, and respond rapidly to change.

In the context of Jordan’s retail sector, EVD is especially relevant. Retailers face rising uncertainty due to currency instability, shifting trade policies, changing consumer preferences, and limited institutional support. Under such conditions, I4.0 technologies can help firms adapt by enabling real-time data analysis (BDA), automating decision-making (AI), and improving supply chain responsiveness (IoT/CC). However, the benefits of I4.0 for sustainability—such as lower emissions, cost savings, or inclusive service models—are more likely to be realized when firms operate in dynamic contexts that demand agility and innovation.

Therefore, it is argued that EVD positively moderates the relationship between I4.0 practices and all three dimensions of sustainability performance.

In highly dynamic environments, firms are under growing pressure to comply with environmental regulations, reduce waste, and meet consumer demand for green products. I4.0 technologies (e.g., energy-monitoring sensors, BDA for logistics optimization) become more impactful under these conditions, enabling firms to track and reduce emissions in real time [34,35,66]. Thus, the environmental benefits of I4.0 are amplified in volatile settings where sustainability is not just desirable but strategically necessary.

Moreover, under the conditions of market instability and cost uncertainty, I4.0 technologies offer firms tools to maintain profitability through efficiency and adaptability. Real-time analytics, predictive demand planning, and supply chain automation reduce costs and support operational continuity [21,51]. The RBV suggests that firms capable of leveraging these technologies in dynamic contexts can develop rare, inimitable capabilities that sustain economic performance despite external shocks [24,42].

In addition, in unstable environments, firms face growing social pressures related to job security, equitable service delivery, and employee well-being. I4.0 technologies can support social sustainability through digital upskilling, safer work environments (e.g., automation of hazardous tasks), and better access to services via online platforms [20,29]. When EVD is high, firms are more likely to deploy I4.0 tools to maintain social legitimacy and stakeholder trust, thus strengthening the SOS dimension.

Thus, based on theoretical reasoning and empirical evidence, EVD is expected to moderate the relationships between I4.0 practices and each dimension of sustainability performance—ENS, ECS, and SOS. Therefore, the following hypotheses are proposed:

H4: 

EVD moderates the positive relationship between I4.0 practices and ENS in the retail sector of Jordan.

H5: 

EVD moderates the positive relationship between I4.0 practices and ECS in the retail sector of Jordan.

H6: 

EVD moderates the positive relationship between I4.0 practices and SOS in the retail sector of Jordan.

The conceptual framework of the study seeks to explore the interlink between the adoption of I4.0 and the Jordanian retail sector’s sustainability performance. The study consists of three major dimensions of sustainability—ENS, ECS, and SOS—as dependent variables. The independent variable of the study is I4.0 and EVD is the moderating variable in the model. Figure 1 illustrates the conceptual model of the study.

3. Methodology

3.1. Research Design

The quantitative cross-sectional survey is used in the study to explore the impacts of I4.0 practices on environmental, economic, and social sustainable performance in the Jordanian retailing industry. The quantitative method suits this study the most as it allows the study to examine the direction and strength of the relationship between the variables without controlling them [67]. With the help of analyzing relationships among I4.0 adoption, EVD, and sustainability, the research seeks to establish valuable information that can guide future planning for digital transformation in Jordan’s retail sector. We have chosen this type because it provides a “snapshot” of the relationships between variables at one point [68]. Compared to longitudinal studies, which monitor change over time, the cross-sectional design is a quicker and less expensive way of addressing the research questions within the available time and resources. The survey design is helped by its potential to provide quantitative data from a relatively large sample of retail businesses. Surveys, when effectively designed, have the potential to provide standardized data on attitudes, perceptions, and practices, thus enabling statistical analysis and hypothesis testing [69]. It is, however, important to take note of the weakness of a cross-sectional survey design. While it can determine relationships among variables, it cannot infer causality as strongly as experimental research [70]. In order to circumvent some of the weaknesses, the research will employ rigorous questionnaire design and data analysis processes. The questionnaire will be derived from existing measures and scales, which will provide content and construct validity.

To summarize, the survey cross-sectional design is selected due to its feasibility, efficacy, and suitability in accumulating quantitative data for examining the relationships among I4.0 practices, sustainability, and EVD in Jordan’s retail sector. Notwithstanding acknowledging its limitations, the research will employ rigorous methodological procedures for maintaining the validity and reliability of the findings.

3.2. Population and Sample

The population represents the whole community, while the sample is part of it [71]. The retail sector in Jordan is heterogeneous, from multinational giants to micro-sized local companies and SMEs. According to The Jordan Times [72], Jordan’s retail sector consisted of more than 12,800 active retail stores, traditional stores, shopping malls, and e-commerce businesses. However, the population of the study includes large businesses that have multiple sectors and look forward to a broader understanding of the challenges and opportunities of implementing I4.0 technologies. Alafi et al. [73] revealed that the Jordanian retail sector is dynamic in its nature, with dominance from some large companies due to their capital investment, experience, and marketing strategies.

This study specifically targeted large retail companies in Jordan, defined based on the classification provided by the Central Bank of Jordan (CBJ) and GIZ [74]. According to their criteria, large firms are those that employ more than 100 full-time employees and/or have an annual turnover exceeding JOD 5 million. These criteria were applied during the sampling process by first consulting national business directories and chamber of commerce records to identify firms that met at least one of these thresholds. In addition to these formal criteria, firms were further screened for evidence of operational scale (e.g., number of branches, online presence, or logistics networks) and a demonstrated interest in or current use of I4.0 technologies such as automation, artificial intelligence (AI), or advanced analytics.

These large firms represent the most advanced segment of the Jordanian retail sector, characterized by greater digital maturity, higher investment capacity, and stronger strategic orientation toward technological innovation. Their relatively greater ability to adopt and sustain I4.0 practices makes them an appropriate focal point for early-stage research on digital transformation and sustainability. Although large companies make up only 0.5% of all enterprises in Jordan—around 881 in total—this figure includes all sectors; the actual number of large retail firms is notably smaller.

Nonetheless, we acknowledge that excluding small and medium-sized enterprises (SMEs), which comprise 99.5% of the country’s enterprises, limits the generalizability of our findings. While large firms offer crucial insights into I4.0 adoption trajectories, future research should incorporate SMEs to provide a more representative understanding of the retail sector’s digital transformation at scale.

For the sake of this study, a purposive sampling technique will be used in selecting large retail firms in the Jordanian market. Purposive sampling is appropriate in this case [75] because the study is aiming for a specific group of firms, those that have the potential to implement I4.0 technologies due to their size and resources. The method helps the researcher to target the firms most likely to have the infrastructure and technological capacities to implement I4.0 innovations. Purposive sampling, according to Ponce and Pagán-Maldonado [76], is typically used where there is the necessity for a particular group of participants since there are specific characteristics the group has, which are directly linked to the study aim.

This study focused on two major retail subsectors in Jordan: food and beverage (F&B) and fashion. These sectors were selected based on both theoretical and practical considerations. From a theoretical standpoint, the F&B and fashion sectors are characterized by high consumer interaction, fast inventory turnover, and strong exposure to market fluctuations—conditions that make them highly relevant for studying I4.0 adoption and its sustainability impacts in dynamic environments [31,38]. These sectors typically rely on demand forecasting, agile supply chains, and customer-centric digital innovations, which are directly aligned with the core capabilities offered by I4.0 technologies such as AI, IoT, and data analytics [9,24,25]. Practically, the F&B and fashion sectors represent two of the most competitive and digitally active segments within Jordan’s retail landscape, and they were among the first to experiment with technology-enabled solutions for inventory management, omnichannel delivery, and consumer personalization. While we acknowledge that other retail subsectors—such as electronics, home goods, and pharmacies—also play important roles in the economy, our goal was to focus on sectors where I4.0 implementation is more visible and where its implications for sustainability could be meaningfully assessed. Future studies should extend this research to cover a broader range of retail categories to enhance generalizability and sectoral comparison.

The large retail companies in the food and beverage (F&B) and fashion sectors are few in Jordan, and the study will target approximately 100 employees from these most significant sectors. Considering this, the sample will be representative of the larger players in the retail sector, with a focus on those companies most likely to have adopted or be adopting I4.0 technologies. By focusing on these sectors, which are among the most digitalized, the study will have full exposure to how I4.0 practices lead to sustainability in the retailing business in Jordan. The selection of a sample of 100 employees, particularly from technology decision-makers and implementers, will ensure the in-depth and focused investigation of the relationship between the implementation of I4.0 and the performance of sustainability in such vibrant and powerful sectors.

To address concerns regarding sample size adequacy for structural equation modeling (SEM) with moderation analysis, we acknowledge that a sample of 100 may be considered relatively modest. However, recent studies suggest that partial least squares SEM (PLS-SEM), the method employed in this study, is robust for small to medium sample sizes and particularly suitable when the research model involves complex constructs or exploratory objectives [77]. According to statistical guidelines by Hair et al. [77], a sample size of 100 exceeds the minimum threshold based on the “10-times rule” (i.e., 10 times the maximum number of inner model paths pointing at a latent construct). Additionally, post hoc power analysis using G*Power 3.1 indicates that, with an effect size of 0.15, α = 0.05, and a power level of 0.80, a minimum of 92 cases is required to detect moderation effects with up to three predictors. Thus, the current sample size (n = 100) is deemed statistically adequate for the study’s analytical goals.

To assess the potential for common method bias (CMB), which may arise from the use of self-reported, single-source data, Harman’s single-factor test was conducted. All items from the main constructs were entered into an exploratory factor analysis using an unrotated principal component method. The results revealed that the first factor accounted for 32.4% of the total variance, which is well below the commonly accepted threshold of 50% [78]. This suggests that common method bias is not a significant concern in this study. Additionally, steps were taken during questionnaire design, such as ensuring respondent anonymity and varying item wording, to further mitigate the risk of response bias.

3.3. Data Collection

Data collection for this study was conducted with the aid of an e-questionnaire that was circulated online for two months, January and February of the year 2025. The e-questionnaire was utilized for its capacity to access the broad array of participants, considering the context of Jordan where the retail market comprises large corporations as well as the general group of individuals who take part in retail decision-making. The online medium allows for cheap, fast, and easily accessible data collection [79]. The questionnaire was sent via email and WhatsApp, which are common media of communication utilized by professionals in Jordan, to obtain maximum response rates and also offer ease to the respondents. Utilizing social media platforms and WhatsApp is a very effective tool in a developing economy like Jordan, where digital accessibility is increasing yet the application of conventional face-to-face data collection tools can be time-consuming and expensive.

The e-questionnaire was administered in the English language, which is the official business language in Jordan, particularly for large businesses. Because the majority of the intended respondents are executive or senior-level professionals, English proficiency is prevalent, which implied that language would not be an impediment to questionnaire completion. Additionally, having one language version of the questionnaire makes the analysis straightforward and minimizes the possibility of translation errors and excess cleaning processes [80].

The demographic section of the e-questionnaire has six primary questions. Question one asks about gender, with an option of Male or Female. Question two addresses age, with ranges of 18–24, 25–34, 35–44, 45–54, and 55+. Question three ascertains level of education, with options of High school or Diploma, Bachelor’s degree, and Master’s degree/Doctorate. Question four asks about years of experience, with options of 1–5 years, 5–10 years, and More than 10 years. Question five is regarding job level, with options of Entry-level, Mid-level, Senior-level, and Executive/C-level. Question six inquires about the type of retail, with options of F&B and Fashion. These questions are designed to solicit information about participants’ background and work experience to act as context to their responses.

The second part of the questionnaire evaluated the perceptions of the participants regarding I4.0 technologies and their impacts on sustainability. It is related to their perceptions about the application of digital technologies, opportunities, and challenges, and the impact of digital technologies on environmental, economic, and social sustainability in their companies. The questions are written in Likert scales, where the respondents will mention the extent of agreement or disagreement with a set of statements. It is common organizational research practice and ensures that the answers are quantifiable for statistical analysis.

3.4. Measurement of Variables

The study uses the quantitative method in the measurement of the main constructs: I4.0, ENS, ECS, SOS, and EVD. Participants’ views and opinions were gathered through the use of the five-point Likert scale questionnaire (1 = Strongly Disagree to 5 = Strongly Agree). I4.0 was operationalized with eight items taken from Khan et al. [81] concerning the adoption and implementation of digital technology in retail firms, for instance, the following statement: “Our company utilizes digital automation and process control sensors”. ENS was measured with six items taken from Jum’a et al. [52], aimed at the retailers’ effort to minimize their environmental footprint and promote green initiatives. An example statement is “Our company adopts green technology for cleaner production and less material usage.” ECS was measured with six items from Jum’a et al. [52], measuring the retailers’ performance in market share, growth in revenue, profitability, and cost-effectiveness. An example statement is “Our company has increased its market share and growth rate.” SOS was measured with five items exported from Jum’a et al. [52], aimed at the retailers’ commitment to improved customer relationships, creating a better brand image through social responsibility, maintaining employee safety and health, and supporting community welfare. An example statement is “Our company has improved customer relationships through just-in-time delivery and quality products.” EVD was measured using Aftab et al. [82] with six items, measuring the retailers’ perception of the rate and unpredictability of change in their external environment. An example statement is “The failure rate of companies in my industry is high.”

The use of multi-item scales for each variable enhances the reliability and validity of the measures. The scales are designed to reflect the complexity of each construct, enabling an exhaustive measurement of I4.0 adoption, sustainability performance, and EVD. The use of established scales where available and the adaptation of items to suit the specific context of the retail sector ensure the content and construct validity of the measures.

3.5. Data Analysis

The research data collected will be analyzed with assistance of structural equation modeling (SEM), which is an advanced statistical technique used for testing complex relations between observed and latent variables. SEM offers the ability to test a number of hypotheses at a time; thus, it is the most suitable technique for this study, which probes the correlations among a number of independent, dependent, and moderating variables (I4.0 adoption, environmental sustainability, economic sustainability, social sustainability, and environmental dynamism).

For the analysis, SmartPLS 4.1 software is to be used. SmartPLS is one of the leading software for SEM, particularly for studies using partial least squares (PLS) path modeling. It is preferred because of its ability to handle complex models with a large number of variables and its suitability for analyzing data that may not meet the assumptions of traditional SEM methods, such as multivariate normality [83]. SmartPLS has several advantages, such as the ability to perform exploratory and confirmatory factor analyses, which is critical for establishing the measurement model, and its user-friendliness, which makes the process of model specification and evaluation simple [51,83].

4. Analysis and Results

4.1. Demographic Profile

The demographic analysis of the 100 respondents reveals a balanced distribution across several key characteristics. The gender split is nearly equal, with 51% male and 49% female respondents. In terms of age, the largest group is between 25–34 years (33%), followed by those aged 35–44 years (23%) and 18–24 years (16%). Fewer respondents fall into the 45–54 years (19%) and 55+ years (9%) categories, suggesting the majority of participants are in the early to mid-career stages. Educationally, most respondents hold a Bachelor’s degree (83%), while 16% have a Master’s degree/Doctorate, and only 1% have High school or Diploma, indicating a well-educated sample. Additionally, the majority of respondents have extensive experience, with 47% having over 10 years of experience, while 35% have between 1 and 5 years of experience, and 18% possess 5–10 years of experience.

For job levels, 37% of the respondents are at the Senior level, while 26% are at the Mid level. 20% are Entry-level, while 17% are at the Executive/C-level, providing views from across organizational levels. For retail type, the sample is almost evenly split between the Fashion (49%) and Food and Beverage (51%) types, allowing for a comprehensive view across these primary retail types. The gender, age, education, experience, job level, and retail type demographic balance provides a well-rounded foundation for understanding the impact of I4.0 adoption on sustainability in Jordan’s retail industry, as shown in

Table 1. The demographic characteristics of the sample of the study.

Characteristic	Category	Frequency	Percentage
Gender	Female	49	49.00%
	Male	51	51.00%
Age	18–24	16	16.00%
	25–34	33	33.00%
	35–44	23	23.00%
	45–54	19	19.00%
	55+	9	9.00%
Education Level	Bachelor’s degree	83	83.00%
	High school or Diploma	1	1.00%
	Master’s degree/Doctorate	16	16.00%
Experience Level	1–5 years	35	35.00%
	5–10 years	18	18.00%
	More than 10 years	47	47.00%
Current Job Level	Entry-level	20	20.00%
	Executive/C-level	17	17.00%
	Mid-level	26	26.00%
	Senior-level	37	37.00%
Retail Type	Fashion	49	49.00%
	Food and Beverage	51	51.00%

.

4.2. Model Assessment

Table 2. The reliability measures for the five variables under study.

Main Variable	Construct	Item Loading	Cronbach’s Alpha	Composite Reliability	Average Variance Extracted
Industry 4.0 (I4.0)	I4.0 1	0.878	0.958	0.965	0.773
	I4.0 2	0.836
	I4.0 3	0.868
	I4.0 4	0.897
	I4.0 5	0.907
	I4.0 6	0.889
	I4.0 7	0.883
	I4.0 8	0.875
Environmental Sustainability (ENS)	ENS1	0.812	0.760	0.832	0.777
	ENS2	0.873
	ENS3	0.831
	ENS4	0.748
	ENS5	0.814
	ENS6	0.768
Economic Sustainability (ECS)	ECS1	0.836	0.921	0.938	0.716
	ECS2	0.846
	ECS3	0.845
	ECS4	0.897
	ECS5	0.818
	ECS6	0.831
Social Sustainability (ECS)	SOS1	0.832	0.79	0.862	0.576
	SOS2	0.848
	SOS3	0.804
	SOS4	0.803
	SOS5	0.857
Environmental Dynamism (EVD)	EVD1	0.743	0.777	0.837	0.780
	EVD2	0.807
	EVD3	0.834
	EVD4	0.817
	EVD5	0.877
	EVD6	0.859

presents item loadings, Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE) for all the constructs. As can be seen in the table, all item loadings are over the 0.70 benchmark [77], indicative of strong relations between items and constructs. All constructs’ Cronbach’s alphas, a measure of inner consistency, have a value over 0.70 [77], with a range of 0.760 for ENS to 0.958 for I4.0, indicative of high item reliability in terms of each one of them. CR, a measure of inner consistency, is over 0.70 for all constructs, providing additional confidence with regard to the measurement model’s reliability. AVE, a measure of variance in items captured through the construct, is over 0.50 for all constructs [84] and, therefore, indicative of convergent validity. All these indications present strong indications for both the measurement model’s reliability and its validity, opening doors for the analysis of hypothesized relations.

For validity, all the variables have AVE values above the minimum recommended of 0.5, with I4.0 having its AVE as 0.773, and EVD having its AVE as 0.780, both showing good convergent validity. The SOS variable, however, has the lowest AVE of 0.576, which, although acceptable, is just near the minimum, suggesting that there might be some potential for the validity of the construct to be enhanced. Although the AVE value for the SOS construct is 0.576, which is close to the critical threshold of 0.50, it is considered acceptable given the overall strength of other measurement indicators. Specifically, the composite reliability (CR) for SOS is 0.862, exceeding the recommended threshold of 0.70, indicating strong internal consistency. Furthermore, all item loadings for the SOS construct are above 0.80, confirming strong indicator reliability. Discriminant validity was also established using both the Fornell–Larcker criterion and the Heterotrait–Monotrait (HTMT) ratio. As Hair et al. [77] suggest, when AVE is slightly below or near the threshold but other reliability and validity indicators are strong, the convergent validity of the construct can still be considered acceptable. Moreover, the measurement items for SOS were adapted and refined from prior validated studies [51,52,53,57] and are consistent with established frameworks, including the Global Reporting Initiative (GRI) Standards. This theoretical and empirical grounding supports the content and construct validity of the SOS scale, further justifying its suitability for hypothesis testing in this study. Moreover, item loadings for all the constructs also surpass the threshold requirement of 0.7. The Heterotrait–Monotrait (HTMT) values, in

Table 3. The Heterotrait–Monotrait analysis.

	ECS	ENS	EVD	I4.0	SOS	EVD x I4.0
ECS
ENS	0.704
EVD	0.612	0.724
I4.0	0.660	0.675	0.459
SOS	0.790	0.880	0.694	0.581
EVD x I4.0	0.270	0.469	0.429	0.258	0.331

, assess discriminant validity between constructs. HTMT values ideally have to fall below 0.85 [77] in representing distinctly different constructs. The HTMT values in the table assess discriminant validity by verifying the correlations between different constructs. For most pairs, the HTMT values are well below the threshold of 0.85, suggesting good discriminant validity.

The Fornell–Larcker criterion, presented in

Table 4. The Fornell–Larcker criterion.

	ECS	ENS	EVD	I4.0	SOS
ECS	0.846
ENS	0.654	0.881
EVD	0.594	0.643	0.883
I4.0	0.624	0.639	0.456	0.879
SOS	0.730	0.729	0.593	0.546	0.759

, provides additional discriminant validity evidence through a comparison between AVE for each construct and between-construct correlations [84].

From Table 4, we see that the square root of the AVE for each construct surpasses the correlations across the constructs, supporting the discriminant validity. The Fornell–Larcker test, thus, confirms the validity of the discriminant validity criteria for all the study constructs.

Table 5. The collinearity statistics—outer model.

Outer Model	VIF
ECS1	2.570
ECS2	2.953
ECS3	3.190
ECS4	4.283
ECS5	2.644
ECS6	2.619
ENS1	1.194
ENS2	2.369
ENS3	2.263
ENS4	1.507
ENS5	1.153
ENS6	1.828
EVD1	1.699
EVD2	2.012
EVD3	1.728
EVD4	1.386
EVD5	1.231
EVD6	1.387
I4.0 1	4.240
I4.0 2	3.768
I4.0 3	3.263
I4.0 4	4.790
I4.0 5	2.129
I4.0 6	4.554
I4.0 7	3.076
I4.0 8	4.332
SOS1	2.031
SOS2	2.252
SOS3	1.778
SOS4	1.117
SOS5	2.217
EVD x I4.0	1.000

presents variance inflation factor (VIF) values, which assess multicollinearity between the outer model’s indicator variables. Multicollinearity arises when two or several independent variables have a high correlation and, as such, can amplify the coefficients’ standard errors and make it difficult to discern the individual variable impact on the dependent variable [77]. Usually, VIF values over 5 or 10 are utilized to mark problematic multicollinearity [77]. In this case, most VIF values fall below 5 and, therefore, multicollinearity is not a critical problem in the dataset.

From Table 5, most of the values of the VIF are within the acceptable range, which suggests there are no multicollinearity problems. For instance, ECS1 has a VIF value of 2.570, ENS2 has a VIF value of 2.369, and SOS4 has a VIF value of 1.117, all of which are much less than the acceptable value, which suggests that the intercorrelations of the items with the respective constructs are not greatly affected by multicollinearity.

The VIF values for the inner model are provided in

Table 6. The collinearity statistics—inner model.

Outer Model	VIF
EVD -> ECS	1.439
EVD -> ENS	1.439
EVD -> SOS	1.439
EVD x I4.0 -> ECS	1.217
EVD x I4.0 -> ENS	1.217
EVD x I4.0 -> SOS	1.217
I4.0 -> ECS	1.271
I4.0 -> ENS	1.271

. All inner VIF values are less than the acceptable value of 5, demonstrating that there is no severe multicollinearity between the constructs. For example, the paths from environmental dynamism to the constructs of sustainability (EVD → ECS, EVD → ENS, and EVD → SOS) all have the value of 1.439, which falls within the acceptable range, demonstrating that the relationships between EVD and each of the dimensions of sustainability are not highly collinear. Similarly, the interaction term of EVD x I4.0 (which examines the moderating effect of EVD for the adoption of I4.0) has the value of 1.217 for the three dimensions of sustainability (EVD x I4.0 → ECS, EVD x I4.0 → ENS, and EVD x I4.0 → SOS), demonstrating low multicollinearity concern for the interactions.

4.3. Hypotheses Testing

The coefficient of determination (R²) values provides an indication of how well the independent variables explain variance in the dependent variables. In this study, the R² for economic sustainability (ECS) is 0.510, meaning that 51.0% of the variance in ECS is explained by the model, including predictors such as I4.0 practices and EVD. This level of explanatory power is considered moderate to substantial, particularly in behavioral and social sciences, where R² values between 0.33 and 0.67 are generally acceptable [82].

The R² for environmental sustainability (ENS) is 0.596, indicating that 59.6% of the variance in ENS is accounted for by the model. This value is relatively high and suggests a strong predictive capacity, especially in the context of emerging market studies involving multiple constructs. For comparison, Bai et al. [16] reported an R² of 0.49 for environmental outcomes in a similar I4.0-based sustainability framework, while Bag and Pretorius [32] found values ranging between 0.45 and 0.58 in studies on digitalization and circular economy impacts. Thus, the current model performs on par with or slightly better than comparable studies in the literature.

The R² for social sustainability (SOS) is 0.447, indicating that 44.7% of the variance in SOS is explained by the predictors. While this is lower than the values for ENS and ECS, it still falls within the moderate range and aligns with findings in similar contexts where social outcomes are more influenced by external and unobserved factors (e.g., regulatory frameworks, cultural expectations, or labor dynamics) [60,61]. The adjusted R² value of 0.430 confirms this result and suggests that although I4.0 and EVD contribute meaningfully to SOS, additional variables may be required to fully capture the complexity of social sustainability outcomes.

Moving to the path analysis (shown in

Table 7. The results of the SEM analysis.

Hypothesis and Path	Original Sample	Sample Mean	Standard Deviation	T Statistics	p Values	Result
H1: I4.0 -> ENS	0.421	0.438	0.070	5.999	0.000	Accepted
H2: I4.0 -> ECS	0.447	0.491	0.119	3.760	0.000	Accepted
H3: I4.0 -> SOS	0.347	0.380	0.103	3.354	0.001	Accepted
H4: EVD x I4.0 -> ENS	−0.142	−0.131	0.061	2.325	0.020	Accepted
H5: EVD x I4.0 -> ECS	0.012	0.026	0.096	0.121	0.904	Rejected
H6: EVD x I4.0 -> SOS	−0.005	0.009	0.083	0.063	0.949	Rejected

and Figure 2), the analysis revealed a strong positive relationship between I4.0 practices and ENS. The association’s path coefficient between I4.0 and ENS was 0.421, a measure of moderate effect size. The finding reveals that the adoption of I4.0 technology in the Amman, Jordan, retail sector is associated with improved environmental sustainability performance. The association was statistically significant with a p-value of 0.000, much lower than the conventional significance level of 0.05. Additionally, the t-statistic for the association was 5.999, further establishing the statistical significance of the effect. The strong statistical evidence reveals that retailers who practice the active use of I4.0 practices are likely to have improved environmental sustainability performance.

For H2, path coefficient of the relationship between I4.0 and ECS was 0.447, representing a moderate to strong effect size. The result shows the implementation of I4.0 technology in the Jordanian retail industry is associated with higher economic sustainability performance. The relationship was statistically significant since the p-value was 0.000, far below the conventional significance level of 0.05. The t-statistic for the relationship was 3.760, also indicating the statistical significance of the effect (H2). The strong statistical proof supports the hypothesis that the implementation of I4.0 has a positive contribution to economic sustainability.

For H3, the analysis reveals a positive and strong relationship between I4.0 practices and SOS. The association between I4.0 and SOS had a path coefficient of 0.347, indicating a moderate effect size. The finding reveals that the use of I4.0 technology in the Jordanian retail industry is accompanied by improved social sustainability performance. The association was statistically significant, as revealed through a p-value of 0.001, lower than the typical significance level of 0.05. The t-statistic for the association was 3.354, further establishing statistical significance for the effect. The evidence confirms the hypothesis that the use of I4.0 leads to social sustainability in a positive direction. The standard deviation for the association was 0.103, providing a measure of the variability in the effect.

Moving to the indirect relationship (moderation), H4 (EVD moderates the positive relationship between I4.0 practices and ENS in the Jordanian retail sector), the result is a statistically significant and weak moderation effect. The interaction term value EVD x I4.0 → ENS in the original sample (O) is −0.142, and it represents a negative moderation effect on the association between I4.0 adoption and environmental sustainability on the part of EVD). The p-value 0.020 is less than the standard cutoff value 0.05 and, hence, indicates that the moderation effect is significant but quite weak. This result shows that with increased EVD, the positive relationship between I4.0 adoption and environmental sustainability is reduced but not eliminated. The negative moderation shows the possibility that in extremely dynamic environments, the impact of I4.0 on environmental sustainability will be reduced to a certain extent.

For H5 (EVD moderates the positive relationship between I4.0 practices and ECS in the Jordanian retail sector), a statistically significant but weak moderation effect is shown through the findings. The original sample (O) for the interaction term EVD x I4.0 → ECS is 0.012 and reflects a very slight positive influence of EVD on the relationship between I4.0 adoption and economic sustainability. Additionally, the p-value of 0.904 is much higher than the commonly accepted level of 0.05, further confirming the non-significance. The implication is that there is no moderating effect that is significant on the I4.0 adoption and economic sustainability relationship by EVD.

For H6 (EVD moderates the positive relationship between I4.0 practices and SOS in Jordan’s retail industry), the results indicate that the moderation effect is not statistically significant. The original sample (O) value of the interaction term EVD x I4.0 → SOS is −0.005, suggesting an extremely trivial negative impact of EVD on the relationship between I4.0 adoption and social sustainability. The value for the T statistics (|O/STDEV|) is 0.063 and is far lower than the critical value of 1.96, suggesting that the moderation effect is not statistically significant. The p-value of 0.949 is far higher than the conventional significance level of 0.05 and further supports the insignificance. The result suggests the absence of any significant moderating influence of EVD on the relationship between I4.0 practices and social sustainability.

5. Discussion of Results

The positive impact of I4.0 on ENS aligns with the theoretical concepts guiding this study and affirms the value of adopting digital technologies for environmental sustainability in resource-constrained settings. In Jordan’s retail sector, where energy costs are high, infrastructure is fragmented, and environmental regulation is evolving, the use of I4.0 tools—such as real-time energy monitoring, predictive maintenance, and AI-based optimization—helps firms reduce operational waste and improve eco-efficiency. This supports the core idea of the CSV framework [39], which argues that firms can simultaneously pursue profitability and societal value. In a developing country context, the CSV contribution is particularly meaningful, as retail firms in Jordan must balance profit pressures with growing social and environmental expectations, often with minimal policy support.

The findings also extend the RBV by demonstrating that digital technologies, when integrated into organizational routines, act as valuable and inimitable resources that contribute to long-term environmental performance [41]. In line with Bai et al. [9] and Oláh et al. [20], our results show that I4.0 capabilities—especially when strategically implemented—enhance the firm’s ability to meet environmental goals despite institutional voids. This is critical in Jordan, where retailers face limited government incentives for green innovation but can still gain a competitive edge by internally embedding I4.0-driven sustainability practices.

Regarding H2, the study reinforces the dynamic capabilities view as outlined by Teece [42], emphasizing that the ability to sense, seize, and reconfigure internal resources determines how effectively firms can leverage I4.0 for economic gains. The findings show that in Jordan’s retail sector, I4.0 technologies like AI-based forecasting and automated inventory management contribute significantly to ECS by improving efficiency and lowering operational costs, which is essential in an economy marked by high inflation, supply chain disruptions, and limited access to capital. These results reflect RBV’s premise that technology must be embedded and adapted, not simply acquired. Although automation poses labor market risks, as Schwab [54] notes, in Jordan’s context of youth unemployment and underemployment, I4.0 can also drive cost savings and enable new forms of economic participation if accompanied by upskilling strategies.

In terms of SOS (H3), the findings highlight that I4.0 practices positively influence workforce well-being, customer inclusivity, and broader societal engagement. In the Jordanian context, where retail employment is a major source of income and labor protections are limited, I4.0 tools like IoT-based workplace monitoring, digital HR platforms, and personalized service applications help create safer, more inclusive, and responsive retail environments. These outcomes are consistent with CSV’s emphasis on societal value creation and RBV’s focus on human-centered capability development [19,55]. This is particularly relevant in Jordan, where the legitimacy and long-term adoption of technological innovation—according to Abaddi [59]—hinges not only on financial outcomes but also on social acceptability.

A key contribution of this study lies in the nuanced finding for H4. While previous studies (e.g., Shahzad et al. [34]; Rehman et al. [35]) reported a positive moderating role of EVD in enhancing the I4.0–ENS link, our findings show a negative moderation effect in Jordan’s retail sector. This contradiction deserves deeper reflection. One possible explanation lies in the resource strain hypothesis: In highly dynamic environments—such as Jordan’s politically and economically unstable retail sector—firms may divert resources away from long-term sustainability efforts toward short-term survival strategies. Regulatory unpredictability, fluctuating consumer demand, and frequent policy shifts may reduce firms’ commitment to green investments, even if they possess the technological tools. As Schilke [64] noted, dynamic capabilities yield optimal results in moderately dynamic conditions, where firms can adapt without being overwhelmed. In Jordan’s case, the volatility may be so high that it disperses managerial attention and weakens the strategic focus needed to harness I4.0 for environmental sustainability. Moreover, sustainability-oriented digital solutions often require upfront investments, which firms may delay or forego during uncertain periods.

Contrary to expectations, H5 and H6 were not supported. The moderation effect of EVD on the I4.0–ECS relationship was statistically insignificant. This suggests that economic benefits from I4.0—such as cost reduction, automation efficiency, and streamlined logistics—may be less sensitive to environmental volatility. Once these technologies are embedded, they tend to yield efficiency gains regardless of external uncertainty. This finding differs from Schilke’s [64] inverted-U proposition and suggests that in Jordan, economic sustainability through I4.0 may be perceived as a core business objective, pursued with or without stable external conditions.

Similarly, the non-significant moderation for H6 implies that social sustainability outcomes—including workplace safety, inclusivity, and employee satisfaction—are relatively stable across different levels of EVD. This could be due to the fact that I4.0-enabled social practices (e.g., digital HR platforms, flexible scheduling, remote support tools) often stem from internal policy choices and are less reactive to market volatility than environmental investments. In Jordan, where the social dimensions of sustainability are increasingly prioritized by both employees and consumers, firms may continue implementing such initiatives irrespective of external turbulence. This divergence from prior studies, such as Schilke [64], may reflect the cultural and institutional specificity of the Jordanian market, where organizational resilience is often built internally due to limited regulatory support.

These findings offer meaningful extensions to the existing literature on Industry 4.0 and sustainable performance. In contrast to Lin et al. [85], who found that digital adoption enhances carbon performance through dynamic capabilities, our study reveals that in the context of Jordan’s retail sector, environmental dynamism negatively moderates this relationship. This suggests that under conditions of regulatory instability and economic uncertainty, firms may divert attention away from long-term environmental initiatives to prioritize short-term survival strategies—a result that challenges the assumption that environmental turbulence always enhances the value of digital transformation. Unlike Karmaker et al. [86], who emphasized the mediating role of green supply chain and circular economy practices in the garment sector of Bangladesh, our study demonstrates that I4.0 technologies can independently generate improvements in all three sustainability dimensions without requiring fully developed green infrastructures. This underscores a more pragmatic, modular approach to digitalization in resource-constrained contexts. Furthermore, while Yavuz et al. [87] reported mixed effects of sustainability practices as mediators or moderators in Turkish technology regions, our findings offer a novel contribution by revealing that economic and social sustainability outcomes in retail are relatively stable across varying levels of environmental dynamism. This suggests that internally driven I4.0 practices such as AI-based planning or digital HR tools can yield consistent value even in volatile environments. Collectively, these results contribute to the refinement of dynamic capabilities theory and the RBV by showing how external context shapes the effectiveness of technological resources and capabilities in emerging market settings.

Novel Insights Emerging from This Study

A key novel insight of this study is the identification of a negative moderation effect of EVD on the relationship between I4.0 adoption and environmental sustainability (ENS) in the Jordanian retail sector. This finding contrasts with prior studies that reported a positive or curvilinear moderating effect of dynamic environments. In our context, high levels of environmental volatility marked by policy instability, unpredictable demand, and fluctuating regulations appear to overwhelm managerial attention and strategic coherence, thereby weakening the effectiveness of sustainability-oriented digital investments. This aligns with an argument that dynamic capabilities yield optimal outcomes under moderately dynamic conditions and contributes to theory by refining our understanding of how external turbulence can sometimes constrain rather than enhance the returns from digital transformation.

Another important contribution lies in the discovery that economic and social sustainability outcomes associated with I4.0 adoption appear to be relatively stable across different levels of environmental dynamism. While environmental sustainability is affected by external volatility, our findings show that firms continue to benefit economically from operational efficiencies and cost savings such as reduced inventory waste and automated logistics once I4.0 systems are embedded. Similarly, digital HR tools, workplace monitoring, and service personalization platforms contribute to enhanced worker well-being, inclusivity, and customer responsiveness regardless of external conditions. This differential sensitivity of sustainability dimensions is a novel theoretical insight, suggesting that economic and social outcomes may be more internally driven and resilient to market uncertainty than environmental initiatives.

Furthermore, this study offers a contextualized application of the CSV and RBV frameworks in a developing country setting. While CSV emphasizes the alignment of societal and business objectives, and RBV highlights the role of internal resources in sustaining competitive advantage, few studies have empirically demonstrated how these frameworks interact under institutional voids. Our findings suggest that I4.0 technologies that are integrated into organizational routines can become valuable, rare, and inimitable resources, even in the absence of strong regulatory or market incentives. At the same time, their ability to generate shared value is evident in how they enhance both firm-level performance and broader societal outcomes, particularly in retail environments where employment and community engagement are deeply intertwined.

Finally, this research contributes to the literature by focusing on the underexplored retail sector in a developing economy, where digitalization strategies are constrained by limited resources, fragmented infrastructure, and evolving consumer expectations. Most existing I4.0 studies center on manufacturing or high-tech industries; in contrast, our study shows that retail firms, especially in emerging markets like Jordan, can leverage modular, scalable digital tools to make incremental but meaningful progress toward sustainability. This sectoral and geographical specificity not only expands the empirical landscape of I4.0 research but also provides practical blueprints for retail managers in similar settings seeking to balance cost, efficiency, and sustainability under systemic constraints.

Thus, this study contributes to CSV and RBV theory by showing how technological capabilities and sustainability outcomes are conditioned not only by firm-level resources but also by context-specific external dynamics. Jordan’s retail sector—shaped by economic fragility, limited policy continuity, and emerging digital infrastructures—offers a distinctive environment where I4.0 adoption is possible but not uniformly effective. The negative moderation effect of EVD on ENS emphasizes the need for policy stability, targeted investment incentives, and capacity-building to support retailers in aligning digital transformation with long-term environmental and societal goals.

6. Conclusions

This study aims to understand how adoption of I4.0 technologies, such as IoT and AI, impacts social, economic, and environmental sustainability in the Jordanian retail sector. The research also explores how dynamic business environment moderates these relationships. To achieve this, quantitative approach, collecting data from 100 retail employees in Jordan is employed. Hypothesis 1 (H1), which posited that I4.0 practices influence ENS positively, was greatly supported with a significant original sample (O) value of 0.421, a T-statistics value of 5.999, and a p-value of 0.000, indicating a positive and strong influence. Similarly, Hypothesis 2 (H2), positing that I4.0 contributes to ECS positively, was supported because the evidence showed an O value of 0.447, a T-statistics value of 3.760, and a p-value of 0.000, indicating increased operating efficiency and profitability in the retail sector through the use of I4.0. Hypothesis 3 (H3), positing a positive influence of I4.0 on SOS, was equally supported with an O value of 0.347, a T-statistics value of 3.354, and a p-value of 0.001, indicating better worker welfare, inclusiveness, and customer satisfaction in the retail sector through the use of I4.0.

In terms of the moderation effect of EVD, Hypothesis 4 (H4), which suggested that EVD moderates the relationship between I4.0 and ENS, was partially supported, with a negative moderation effect. The O value for EVD x I4.0 → ENS was −0.142, with a T-statistics value of 2.325 and a p-value of 0.020, indicating that in highly dynamic environments, the effectiveness of I4.0 in promoting environmental sustainability is slightly weakened. Hypothesis 5 (H5), proposing that EVD moderates the relationship between I4.0 and ECS, was rejected, as the O value of 0.012, T-statistics of 0.121, and p-value of 0.904 showed no significant moderation. Likewise, Hypothesis 6 (H6), which examined the moderating effect of EVD on the relationship between I4.0 and SOS, was also rejected, as the results for the interaction term EVD x I4.0 → SOS were statistically insignificant (O = −0.005, T-statistics = 0.063, p-value = 0.949).

6.1. Managerial Implications

In the rapidly evolving global retail landscape, the integration of I4.0 technologies and sustainability-oriented practices is no longer optional; it is considered a strategic necessity. The findings of this study offer several managerial implications for both Jordanian retailers and international retail managers seeking to align digital transformation with sustainable performance.

For retail managers in Jordan, the results highlight the importance of a phased and context-sensitive digital roadmap. Rather than pursuing full-scale automation, which may be financially and operationally unfeasible for many firms, managers should prioritize the adoption of cost-effective technologies with high operational impact, such as IoT-enabled inventory tracking systems, AI-based demand forecasting tools, and cloud-based point-of-sale platforms. These tools are relatively easier to implement, require less infrastructure, and deliver measurable gains in efficiency and resource optimization, which is the key to achieving environmental and economic sustainability.

Given the volatile regulatory and economic environment in Jordan, retailers should also advocate for government-backed incentives such as subsidies, low-interest loans, or public–private partnerships that de-risk digital transformation. Collaborating with industry associations and academic institutions to co-develop sector-specific digital capability frameworks and training curricula would ensure that upskilling initiatives are not only technically relevant but also localized. Investing in training for middle management and front-line staff to operate and interpret I4.0 systems is crucial, especially considering the current digital literacy gap.

Moreover, data privacy and cybersecurity frameworks should be embedded early in the digital transformation process. As retail operations become more interconnected, ensuring ethical standards for data use and compliance with evolving consumer protection regulations will be essential to building consumer trust both in Jordan and globally.

Internationally, the findings also offer relevant implications. In any retail market, in a developed or emerging country, digital transformation initiatives should consider the firm’s technological maturity, resource base, and institutional context. Retail managers across countries should adopt a modular, scalable approach to I4.0 adoption, prioritize measurable sustainability metrics, and integrate ethically guided innovation into their strategic agenda. Regardless of location, aligning digital investments with customer-centric sustainability goals enhances not only efficiency but also brand differentiation and stakeholder value. From a theoretical perspective, these implications align with the CSV and RBV frameworks, showing that I4.0 investments become truly valuable when tailored to internal capabilities and external constraints. Combining digital enablement, workforce development, sustainability orientation, and institutional engagement is critical for long-term competitive advantage in the digital era.

Moreover, the findings of this study offer actionable implications for both practitioners and society, particularly in developing economies like Jordan where digital infrastructure and policy support remain limited. For retail managers, the results suggest that digital transformation should be approached through phased and scalable implementation strategies. Prioritizing low-cost, high-impact technologies such as cloud-based point-of-sale systems, IoT-enabled inventory management, and AI-driven forecasting can deliver measurable gains in operational efficiency, waste reduction, and cost savings, which are key enablers of both environmental and economic sustainability.

Importantly, these benefits appear to persist even under external volatility, suggesting that digital investments in these areas are relatively robust. In terms of social sustainability, I4.0 tools such as digital HR platforms and workplace monitoring applications improve workforce safety, inclusivity, and job satisfaction, which is especially valuable in a labor-intensive sector like retail where employment quality directly affects community welfare. These findings also carry important societal implications: They show that firms can align profit motives with societal value creation, reinforcing the relevance of the CSV approach in developing countries. Policymakers are encouraged to support this alignment through incentive structures such as targeted subsidies, low-interest digitalization loans, or public–private training initiatives.

Lastly, the results are also instructive for international retail managers and multinationals operating in emerging markets: Successful digital transformation requires sensitivity to institutional conditions, organizational readiness, and workforce capacity. Embedding ethical data practices and aligning digital initiatives with customer-centric sustainability goals can simultaneously strengthen brand equity and long-term competitive positioning.

6.2. Theoretical Implications

This study offers several theoretical contributions by integrating CSV and RBV to explain how I4.0 adoption influences ENS, ECS, and SOS in the retail sector of a developing country. By empirically validating that I4.0 practices can improve ENS and ECS, the study extends the RBV framework, emphasizing that digital technologies, when strategically embedded, serve as VRIO resources that enhance operational performance and competitiveness. Unlike much of the existing RBV literature focused on manufacturing, this research demonstrates its applicability in customer-facing, service-driven retail environments in emerging markets.

The study also contributes to CSV by illustrating how digital transformation through I4.0 enables firms to pursue economic goals while simultaneously advancing social and environmental objectives. A novel insight is the finding that SOS remains unaffected by EVD, suggesting that social value creation through I4.0 is more internally anchored and less susceptible to environmental fluctuations. This expands CSV theory by indicating that firms may need distinct strategies to safeguard SOS, even when external conditions are unstable.

Furthermore, by introducing EVD as a moderator, this study enhances the theoretical understanding of how external volatility influences the I4.0–sustainability relationship. Contrary to prior studies that suggest positive moderation (e.g., Shahzad et al. [34]), the finding of a negative moderating effect of EVD on the I4.0–ENS link reveals that excessive environmental turbulence can hinder long-term environmental returns from digital investments. This contradiction challenges existing assumptions and suggests a need to reevaluate the conditions under which I4.0 delivers sustainable value.

Accordingly, the study advances theoretical discourse by showing that the impact of I4.0 on ENS, ECS, and SOS is not uniform and is contextually shaped by EVD. It encourages future research to explore the dynamic interplay among firm capabilities (RBV), value co-creation strategies (CSV), and environmental uncertainty (EVD) across varied institutional and sectoral landscapes.

6.3. Limitations

First, the study only focuses on large retail companies in Jordan. Although this focus provides detailed insight into the extent to which I4.0 technologies are being adopted within these specific sectors, this limits the generalizability of the findings to other retail sectors or smaller firms (SMEs). Second, the cross-sectional nature of the study means that the study is not able to capture the long-term effects of I4.0 adoption or to control for the potential for changes in the determinants of sustainability over time. Future studies using a longitudinal design would be beneficial to explore the long-term impact of I4.0 adoption on sustainability outcomes. Additionally, respondents may have biased or limited perceptions of the application of I4.0 technologies, especially if they are not directly involved in the decision-making process in terms of the adoption of technologies. Additionally, the moderating effect of EVD was not strong and, therefore, it is likely there are additional factors with stronger moderating effects in the I4.0–sustainability relationship that call for research. Finally, the study is focused solely on the retail sector in Jordan, which, while providing valuable insights into the specific challenges and opportunities of I4.0 adoption in a developing economy, limits the generalizability of the findings to other nations or regions.