Big Data Analytics as a Driver for Sustainable Performance: The Role of Green Supply Chain Management in Advancing Circular Economy in Saudi Arabian Pharmaceutical Companies

Abstract

Facing growing sustainability challenges and the critical priority of digital transformation, this study explores, through the lens of the dynamic capability view, the links between big data, sustainable performance, and green supply chain in a circular economy logic, filling a notable gap in emerging markets, particularly the pharmaceutical sector. Our study proposes an original conceptual model linking big data analytics to the circular economy, tested with 275 employees from the Saudi pharmaceutical sector. The results, obtained through state-of-the-art PLS-SEM modeling, indicate a significant positive impact of big data analytics on sustainable performance and green supply chain management within the circular economy framework. The study also reveals the crucial mediating role of sustainable performance and green supply chain management in the relationship between big data analytics and the circular economy. Our study proposes an integrated framework for understanding how digital technologies support the circular economy in emerging markets, with practical implications for pharmaceutical sector actors and policymakers, in line with Saudi Arabia’s Vision 2030.

1. Introduction

Global events in recent years have triggered significant shifts in global health and economic systems [1]. The COVID-19 pandemic, for example, exposed the fragility of traditional supply chains and emphasized the urgent need for resilient and sustainable business models.

In response, industries have adopted three key strategies—digitalization, sustainability, and adaptability—that serve as crucial drivers of long-term competitiveness [2,3,4].

The rapid technological evolution since the mid-20th century, especially in big data analytics and artificial intelligence, has transformed business models, pushing firms to increasingly rely on advanced technologies to boost operational efficiency and competitive advantage [5]. Big data analytics (BDA), in particular, plays a strategic role by improving investment decision quality, optimizing resource use within circular economy (CE) frameworks, and enhancing sustainable competitiveness [6,7]. However, leveraging big data requires not only technological infrastructure but also strong organizational capabilities and human skills [8,9,10], aligning with the socio-technical perspective that emphasizes the critical role of human–AI interaction quality in determining technology effectiveness [11]. The adoption of BDA to advance sustainable outcomes remains a complex undertaking, requiring the presence of complementary organizational capabilities to effectively unlock its full potential.

Previous studies have investigated the integration of various technology-driven solutions to enhance the sustainable supply chain performance [9,12,13,14], as well as CE practices [11,15,16]. Similarly, others have investigated the direct relationship of CE on sustainable performance [17,18], and the influence that sustainable supply chain has on CE-targeted performance [18]. However, despite these parallel research streams, the combined relationship between BDA, green supply chain management (GSCM), sustainable performance (SP), and CE still remains largely unexplored.

Moreover, despite growing academic interest in CE, current literature reveals fundamental theoretical inconsistencies that limit our understanding of the integration between BDA and CE practices. Empirical studies have reported mixed findings regarding the relationship between BDA and firm performance, with some studies identifying a positive association, while others reveal negative or insignificant effects [19]. Further, some scholars contend that although BDA contributes to enhancing supply chain resilience, it is insufficient for addressing long-term strategic challenges, requiring companies to adopt concepts that emphasize sustainability [11].

These contributions often lack integrated theoretical frameworks explaining the mechanisms through which digital capabilities enable CE transitions. No study has investigated the mediating role of both SP and GSCM in the relationship between BDA and CE.

This knowledge gap is particularly prevalent in highly regulated and knowledge-intensive industries, where data analytics applications face unique challenges related to compliance requirements, stakeholder complexity, and specialized domain knowledge.

Our study responds to the call for the application of integrated frameworks that simultaneously address efficiency, resilience, and sustainability [20].

Drawing on the resource-based view (RBV) theory [21] and the dynamic capabilities (DC) framework [22], this study conceptualizes BDA as a strategic organizational capability that enables firms to reconfigure their operational processes toward implementing a CE. In contrast to prior research that views BDA merely as a standalone technological tool, this study theoretically frames it as a socio-technical system, in which human capital developed by managerial knowledge, skills, and expertise, is integrated with AI capabilities to drive sustainable transformation [23,24].

This theoretical positioning addresses a critical gap in the literature, as studies on digital transformation have predominantly focused on technological determinism, often overlooking the complex interplay between human capabilities, artificial intelligence, and organizational processes [25,26].

Our study advances theoretical understanding by integrating the resource-based view (RBV) and dynamic capabilities (DC) theories to develop an integrated framework in which BDA is considered a dynamic capability encompassing the processes of sensing (identifying and assessing opportunities and threats), seizing (mobilizing resources to capture opportunities and mitigate threats), and transforming (reconfiguring and realigning resources) enabled through big data and business analytics, in orchestration with other organizational resources and capabilities, to leverage innovation and respond to business environmental challenges [24,27].

According to the dynamic capabilities view (DCV), firms achieve sustained competitive advantage and advance CE goals by continuously adapting, integrating, and reconfiguring internal and external competencies [22,27,28]. Building on this perspective, this study explains how BDA functions as a dynamic capability, simultaneously enabling SP and GSCM, thereby contributing to the realization of CE objectives.

This approach helps reconcile contradictory empirical findings in the digital transformation literature through comprehensive dual-mediation testing. Furthermore, these insights enable industry professionals to strategically implement BDA, thereby supporting and coordinating the development of SP and GSCM.

Despite its potential, empirical studies on BDA in emerging markets, especially in the context of CE, remain limited. This limitation is particularly critical given that emerging markets face distinctive institutional environments, resource constraints, and regulatory frameworks that may fundamentally alter how digital capabilities operate.

This study aligns with Saudi Arabia’s Vision 2030, which seeks to build a knowledge-based economy through technological innovation and sustainability. Focusing on the pharmaceutical sector, this research investigates how BDA supports CE practices, with SP and GSCM acting as mediators. The study contributes theoretically and practically by providing insights into how BDA can foster long-term performance in emerging markets. To address these goals, the study explores the following research questions:

• To what extent does BDA influence sustainable achievement and the CE?
• How does SP mediate the relationship between BDA and CE?
• What role does GSCM play in mediating the link between BDA and the CE?

The emerging nature of BDA calls for more empirical verifications and replications. Our study makes several innovative contributions to existing literature. First, we conceptually replicate the established links between BDA and SP identified in previous research [15,29,30], while constructively evaluating their strength and generalizability in the specific context of Saudi Arabia. Second, we examine these relationships in the framework of GSCM within a CE, focusing on the pharmaceutical sector in Saudi Arabia, which allows for the exploration of a field still understudied in the literature and provides unique perspectives on Middle Eastern emerging markets, largely underrepresented in current research. To the best of our knowledge, this study is among the first empirical investigations to integrate BDA, SP, and GSCM into a CE framework specifically tailored to the Saudi pharmaceutical industry, an area largely underrepresented in current research. Third, our integrated model simultaneously examines two key mediating variables (SP and GSCM), allowing for a deeper understanding of the mechanisms by which BDA influences the CE. Finally, our study provides practical insights that are consistent with Saudi Arabia’s Vision 2030, thus enriching the literature on digital transformation and sustainability in economies transitioning towards knowledge-based models.

2. Theoretical Framework and Hypotheses Development

2.1. Big Data Analytics as a Dynamic Capability

Dynamic capability view (DCV), an extension of the resource-based view (RBV) theory, underscores the inherently evolving and adaptive nature of organizational resources and capabilities. It provides insights into how organizations can adapt to rapidly changing environments and achieve competitive advantages by integrating, reorganizing, and developing internal and external capabilities [22,27].

Drawing on the dynamic capabilities view (DCV), BDA is conceptualized as a key organizational information processing capability that enhances a firm’s ability to navigate environmental complexity. Specifically, BDA enables firms to sense opportunities and threats, seize them through strategic resource allocation, and transform internal and external processes for sustained competitive advantage [19].

The strategic value of BDA lies not only in its technical sophistication but also in its embeddedness within the broader organizational context that facilitates dynamic capability development [24]. From this perspective, BDA constitutes the infrastructure, data management practices, and talent necessary to transform raw data into actionable business insights [31]. These capabilities span both tangible elements, such as enterprise systems, data integration platforms, and analytics tools, and intangible elements, including data literacy, technical skills, and managerial competence [32]. BDA enables firms to harness both structured and unstructured data from a range of sources, including internal systems and IoT devices, to drive predictive analytics and optimization strategies [33]. Consequently, BDA serves as a critical enabler of strategic initiatives to innovate, adapt, and build sustainable competitive advantage [19].

2.2. Circular Economy

The CE is a restorative and regenerative system focused on optimizing material use, waste reduction, and energy efficiency [29]. Building on the linear economy model, it emphasizes reusing resources and minimizing waste, addressing environmental concerns like pollution and resource depletion [7]. Although the concept dates to the 1970s, it has gained significance in recent years due to resource scarcity and changing consumer behaviors. CE practices include recycling, renewable energy, and materials restoration to reduce environmental impact. The transition from linear to circular economies fosters long-term resilience, competitiveness, job creation, and reduced environmental pressure [29,34]. By focusing on durability and recyclability, companies can lower waste and costs, enhancing sustainability [35,36].

It is important to clarify the distinction between CE and regenerative economy, terms sometimes used interchangeably in the literature. Referring to the article by Piero Morseletto [37], the circular economy primarily focuses on optimizing resource flows and minimizing waste through reuse, recycling, and recovery cycles. The regenerative economy, on the other hand, seeks not only to maintain resources within the economic cycle but also to actively restore and regenerate natural and social systems. In the context of this study, we primarily use the term “circular economy”, while recognizing its regenerative aspects, particularly in the context of the sustainable development of the Saudi pharmaceutical sector.

Unlike the traditional linear economic model characterized by an “extract-make-dispose” approach with little attention paid to resource recovery, the modern CE proposes a transformative paradigm that radically transforms production and consumption cycles to maximize resource value throughout their lifecycle [38,39].

The CE represents a comprehensive approach to addressing environmental challenges, particularly through the elimination of waste and the reduction of pollution. This model emphasizes the optimization of resource value by promoting recycling and the continuous circulation of products and materials. Additionally, it supports the transition to renewable energy sources and sustainable materials that contribute to the restoration of natural ecosystems [7]. Beyond material and energy considerations, the CE also fosters sustainability across industrial processes, technological innovation, and societal behaviors, thereby offering a multidimensional framework for long-term environmental and economic resilience. Recent studies [5,40] have provided foundational definitions of the CE, highlighting its multifaceted nature and offering frameworks that support the operationalization of CE practices in various industries.

Despite the widely acknowledged importance of the CE, its effective implementation depends on several critical factors and conditions [29]. Creating a sustainable economic development effort requires a systematic transformation that, first, promotes long-term resilience within the industrial system—enabling new economic activities that enhance competitiveness, generate employment, and reduce environmental pressures—and second, applies to all natural resources—both biotic and abiotic, including water and land. This transformation represents a successful step toward transitioning from the traditional linear economy to a CE that disassociates economic activity from finite resource consumption [7]. The tenets of the regenerative economy stimulate companies to upgrade and reduce the need for raw materials, accordingly, reducing environmental decline related to raw material extraction. By producing products with respect to durability and recyclability, companies can reduce waste destined for landfills [35]. CE practices contribute to cost reduction and revenue generation through recycling and reusing materials. As such, the CE represents a shift toward a more sustainable economic model [34]. It accentuates resource reduction, reuse, recycling, and regeneration to minimize garbage and accelerate the value derived from each raw material [36].

In the context of the Saudi pharmaceutical sector, adopting CE principles represents a good strategic opportunity to transform environmental challenges into competitive advantages. The resource-intensive nature and high potential for hazardous waste generation in this sector reinforce the interest in applying the CE. BDA also constitutes a strategic lever, providing powerful tools to catalyze this transition by identifying inefficiencies in resource use, optimizing production processes, and facilitating the implementation of waste reduction strategies [38]. Our study particularly examines how the integration of BDA can accelerate the adoption of CE practices in this sector, thus contributing to the achievement of sustainability and economic diversification objectives defined in Saudi Arabia’s Vision 2030.

2.3. Impact of Big Data Analytics on Circular Economy

BDA is used to extract relevant and meaningful information from large datasets. The insights generated by analyzing the available data using big data techniques help to (1) manage a product’s life cycle particularly in monitoring material flows throughout its stages, (2) build mutual supportive and coordinated action aimed at sustainable business operations, (3) mitigate supply–demand mismatches, (4) support strategies aimed at extending product longevity and promoting the reuse and recycling of materials, (5) facilitate proactive maintenance strategies—key components of a circular economy. In this context, BDA plays a crucial role by supplying real-time, data-driven insights to decision-makers, improving system-wide visibility, predicting demand fluctuations, analyzing product usage patterns, and predicting product lifespan [11].

Previous studies have demonstrated that BDA drives the implementation of CE practices. For instance, ref. [41] identifies BDA as a critical enabler of decision-making in CE initiatives focused on process integration and resource optimization. Moreover, ref. [38] implemented a conceptual framework showing how BDA facilitates informed decision-making among stakeholders in establishing the CE. Based on stakeholder theory, their approach emphasizes collaborative data sharing as a determinant of CE transformation. In fact, a CE operates through a bi-directional logistics framework that integrates both forward and reverse logistics processes [42]. The success of this system relies heavily on the efficient exchange of data and information among participating organizations. BDA plays a pivotal role in deriving critical insights from data on CE members, thereby informing decision-making on 3R (Reduce, Reuse, Recycle) strategies and material circularity at all organizational levels. Therefore, BDA-derived insights facilitate process integration and resource sharing, thereby contributing to reduced resource consumption across the entire supply chain. Ref. [43] further emphasizes that BDA enhances predictive maintenance, route optimization, and insights into product usage and customer needs, thereby supporting product life extension and promoting material reuse and recycling. Ref. [44] confirms that BDA can play a significant role in helping firms to achieve green innovation and competitive advantage. Corroborating findings are reported in the study of ref. [45] who underlined that BDA capabilities constitute an effective enabler for CE practices.

The integration of big data into the CE is based on technical mechanisms that align data processing with the principles of reduction, reuse, and recycling [46]. For example, Machine learning algorithms optimize resource use by identifying complex consumption patterns invisible to the human eye. Particularly, in the pharmaceutical industry, predictive analysis anticipates production defects, thus reducing losses of raw materials. Additionally, IoT combined with big data analytics ensures real-time traceability of materials, facilitating their reuse. Finally, data classification techniques allow for better categorization of waste, essential for optimizing recycling processes, particularly in complex contexts such as pharmaceutical chemical waste.

Based on the previous argument, we hypothesize the following:

H1. 

Big Data Analytics has a positive effect on the circular economy.

2.4. Impact of Big Data on Sustainable Performance

Sustainable performance attempts to align economic growth with environmental sustainability, so as to maintain the natural environment and encourage social equity through considering future human needs. This manner motivates companies to apply practices that are friendly to the environment, which in turn achieve mitigation of ecological damage and simultaneously maximize economic benefits [47]. Such an endeavor is increasingly important in the light of a limited resource world [15]. Notably, large-scale data analysis serves as a technological backbone needed for the innovation and efficiency essential to this endeavor. It is of high significance to achieve a substantial shift in our economic, societal, and cultural systems toward facilitating environmental sustainability through equal profound transformation in decision-making and resource utilization [48]. Large-scale data analysis emerges as one of the significant tools in this regard, with its advanced automation, digitization processes, and modern manufacturing techniques that supply large-scale data to reinforce the sustainability of industrial practices. This is the main motivation of the companies’ reliance on eco-friendly practices and BDA [49]. Studies by refs. [33,50,51,52] demonstrated the positive impact of BDA on long-term performance.

Accordingly, we derive the following hypothesis:

H2. 

Big data Analytics positively impacts sustainable performance.

2.5. Impact of Big Data Analytics on Green Supply Chain Management

GSCM refers to the integration of environmental considerations into various stages of supply chain management. This encompasses product design, the sourcing and selection of materials, manufacturing processes, the distribution of final products to consumers, and the management of products at the end of their life cycle [53]. Industry 4.0 technologies have been widely recognized as enablers of sustainable operations and supply chain management. Among these, BDA plays a critical role by enhancing supply chain visibility, flexibility, and decision-making. Over recent years, the utilization of big data in green supply chains has increased significantly, supporting improved forecasting, resource optimization, and waste reduction [54].

For example, following the Coronavirus outbreak, “digital transformation” and “green transformation” came to be as integral parts of the global economic recovery. To achieve green economic growth, companies need to manage green supply chains by leveraging BDA. Basically, BDA allows companies to integrate environmentally relevant elements with efficient information resources to address environmental issues [55].

The application of information analytics in artificial intelligence not only enhances companies’ green performance by reducing information asynchrony and handling complex environmental data [10]. It also provided feedback for decision-making to reinforce GSCM through analyzing and identifying patterns in data, predicting environmental effects, and reducing energy use, all of which led to improved environmental performance [56]. Studies by refs. [56,57] have shown the positive impact of BDA on companies’ green performance. Further, ref. [10] found that big data enhances environmental capabilities and operational performance, offering a new perspective for researchers and practitioners to support green logistics and supply chain projects in achieving higher green performance. Significant studies, notably those of refs. [29,58], have demonstrated the positive influence of BDA on green supply chains.

Based on this, we derive the following hypothesis:

H3. 

Big data Analytics has a positive impact on green supply chain management.

2.6. Sustainable Performance’s Impact on Circular Economy

Sustainable performance can be defined as the environmental, economic, and social achievement combined together in a manner to positively impact the natural environment and society, and provide a competitive advantage for companies as well [59]. In other words, it addresses the needs for economic and social growth alongside environmental safeguarding, which brings about industrial benefits over both the short and long terms [60]. Moreover, it maintains the economic, environmental, and social fields in the production of goods and services, which will ensure economic growth, resource conservation, and the reduction of negative environmental and social impacts [61,62].

CE and sustainability are intertwined, as both strive to develop systems that promote long-term environmental balance, economic viability, and social equity. The circular economy (CE) framework aligns closely with several of the United Nations’ Sustainable Development Goals (SDGs), particularly in its emphasis on resource efficiency, waste reduction, and sustainable production systems. Notably, SDG 7 (“Affordable and Clean Energy”) and SDG 12 (“Responsible Consumption and Production”) directly support the advancement of CE strategies. SDG 7 promotes the transition to clean, renewable energy sources, which is essential for minimizing the environmental impact of production and consumption processes, key tenets of the CE model. Similarly, SDG 12 advocates for sustainable consumption and production patterns, encouraging the reduction of material use, improved product life cycles, and the adoption of closed-loop systems. These goals collectively reinforce the implementation of CE as a pathway toward long-term sustainability across environmental, economic, and social dimensions [63].

Most researchers consider the CE a regenerative element of long-term development and an efficient solution for the transition toward a sustainable system [64], while limited studies [65,66,67,68,69] highlight that sustainable performance is not merely a complement to the circular economy—it is a foundational mechanism that enables its effective realization and long-term success.

Sustainable performance management plays a critical enabling role in the effective implementation of circular economy (CE) initiatives by aligning an organization’s strategic vision, mission, and objectives with operational execution. This alignment is essential for embedding CE principles across all organizational levels, ensuring that sustainability is not an isolated initiative, but a strategic imperative integrated into decision-making processes. Therefore, sustainable performance management serves not only as a measurement tool but also as a strategic bridge. It helps organizations operationalize CE principles by integrating them into performance indicators, resource planning, and process innovation. Thus, sustainable performance management is not only supportive of CE—it is essential for its successful execution [3,51,66]. Companies that integrate CE into their overarching strategy are better positioned to achieve performance gains, yet many struggle to operationalize their strategies circular economy as part of sustainability performance management. Ref. [68] warns that resource efficiency strategies risk diverging from circular economy goals when they are motivated purely by cost-saving considerations instead of being guided by long-term ecological and social priorities. Ref. [70] emphasizes that circularity in companies and the development of practicable CE is achieved by capturing the different facets of sustainability. They point out that the predominant focus within CE discourse has been on the environmental dimension of sustainability, specifically, resource utilization and emissions across all lifecycle stages, while advocating for increased attention to critical social factors. In particular, they underscore the importance of the social aspect of sustainability, such as social cohesion and collaboration, equity, governance, and community engagement, in supporting the circular transition. In this line, Ref. [67] argues that the implementation of CE strategies could be hampered by the insufficient environmental laws and regulations, the lack of mandatory requirements and responsibilities for manufacturers/suppliers, and the lack of effective recycling policies to achieve quality in waste management.

Implementing sustainable performance strategies has been shown to significantly enhance the effectiveness of the CE by promoting resource efficiency, reducing environmental impact, and supporting systemic innovation. These strategies facilitate the transition from linear to circular models by integrating sustainability principles across value chains [65]. Businesses that prioritize sustainable performance build resilience against resource scarcity, achieve cost savings, reduce carbon footprints, and integrate social and environmental concerns into their operations and interactions with stakeholders, thus contributing to achieving a sustainable CE [65,67].

Empirical studies suggest that aligning sustainability performance metrics with circular economy practices leads to more measurable progress toward long-term sustainability goals [65,69].

With respect to the previous, we derive the following hypothesis:

H4. 

Sustainable performance has a positive impact on the circular economy.

2.7. Impact of Green Supply Chain Management on the Circular Economy

Circular economy (CE) represents a regenerative economic model that seeks to replace the traditional “end-of-life” paradigm with strategies focused on reducing, reusing, recycling, and recovering materials across the supply chain, thereby maintaining value and fostering sustainable development. This systemic transformation relies on collaboration among diverse stakeholders [71]. In this context, GSCM and CE emerge as complementary frameworks, both aiming to embed environmental sustainability within supply chain practices. Their overlapping principles, such as resource efficiency, waste reduction, and life cycle thinking, position them as mutually reinforcing approaches that support the transition toward sustainable development. In other words, when companies adopt GSCM as an institutional strategy, resource consumption will be minimized and environmental protection will be achieved, thereby improving CE performance [30]. In the Saudi context, macro-level policies under Vision 2030 actively incentivize circular economy transitions. Our study uniquely explores this interplay of institutional support with digital innovation in the pharmaceutical supply chain. The transition to a CE requires firms to innovate across supply chains, involving sustainable changes from product design to production and delivery. This shift necessitates the transformation of existing business models to align with sustainability goals, emphasizing collaboration within the value network among supply chain partners and customers [67]. During the transition of CE in the supply chains, policy-related barriers such as legislation, taxation, funding, infrastructural, and procurement barriers [72] are one of the important barriers. Macro-level environmental constituencies and institutional policies are critical drivers of change in sustainability policies and practices toward sustainable futures [30]. In the Saudi context, the alignment of corporate initiatives with the objectives of Vision 2030 and the associated government policies and regulations creates an adequate environment for the transformation towards a circular economy.

The transition to a CE generates value in corporate environmental management by implementing closed-loop systems, reverse logistics, product life cycle management, and cleaner production practices. GSCM supports the principles of the circular economy by integrating practices such as reuse, recycling, and end-of-life recovery [67]. Facilitated through reverse logistics, these practices promote responsible consumption and contribute to the reduction of waste, aligning with the goals of sustainability and resource efficiency in supply chain operations [73,74].

In the circular economy, GSCM boosts resource utilization and is regarded as a remedy to tackle environmental issues and consumption patterns across the overall supply chain. Applying and measuring the performance of GSCM is highly significant for survival in an increasingly competitive environment. Within the context of the CE, companies aiming to improve GSCM must continually monitor their performance to integrate CE principles into their GSCM practices [75]. Studies by refs. [8,30,57] have shown that GSCM, environmental performance, green human resource management, and green performance all positively impact the circular economy.

Based on this, we derive the following hypothesis:

H5. 

Green supply chain management has a positive impact on the circular economy.

2.8. Sustainable Performance as a Mediator Between Big Data Analytics and Circular Economy

Despite existing research on the separate links between these concepts, the mediating role of sustainable performance in BDA and the CE remains underexplored. Ref. [20] called for integrative frameworks linking data capabilities to sustainability outcomes. Our study addresses this question by empirically testing this link in the pharmaceutical sector.

Attaining sustainable performance goals, such as efficient raw materials and more sustainable lifestyles, is crucial to achieving a stable future for both present and future generations. In this context, the need for a CE has become increasingly apparent [62]. In fact, the goals of an effective CE, along with the achievement of sustainable performance, can be supported by emerging technologies such as BDA, which can serve as a key enabler of these endeavors. The synergy among the three elements, SP, BDA, and the CE, facilitates the transition to a sustainable future, amplifying the overall benefits [76,77]. A study by [15] revealed that industrial sustainability serves as a mediating variable between BDA and the CE. Additionally, ref. [33] found that the application of CE could positively facilitate the relationship between BDA possibilities and sustainable performance. Ref. [7] demonstrated that big data analytics could enhance sustainable performance in organizations by reinforcing the application of circular economy practices. However, ref. [50] claimed the absence of the mediating role of environmental performance integration between BDA and industrial sustainability.

Based on the previous studies, the author derives the following hypothesis:

H6. 

Sustainable performance mediates the relationship between Big Data Analytics and the Circular Economy.

2.9. Green Supply Chain Management as a Mediator Between Big Data Analytics and Circular Economy

While previous studies have demonstrated the effect of BDA in achieving environmental sustainability [32], others have provided empirical evidence on the specific contribution of BDA to enhance supply chain sustainability, resilience, adaptability, and flexibility [11,78].

It is widely acknowledged that GSCM practices are key drivers in supply chain and operations management, providing the procedural and operational changes necessary to implement sustainable practices. GSCM represents a strategic approach to achieving supply chain sustainability by integrating environmental considerations into supply chain management [79].

Moreover, empirical findings indicate a significant positive effect of BDA capabilities on green supply chain performance [29] as well as on CE practices [45,76]. However, the pathway through which BDA influences sustainable supply chain outcomes is not always direct. For instance, the relationship between BDA and supply chain innovation is mediated by two crucial capabilities of agility and adaptability that enable firms to efficiently meet the challenges of supply chain ambidexterity [78].

GSCM, which incorporates environmental thinking into supply chain practices—from product design to material sourcing and end-of-life management—may serve as a key mediating mechanism that explains the link between BDA and CE. By leveraging insights derived from BDA, organizations can implement GSCM practices more effectively, which in turn supports CE goals such as recycling, reuse, and remanufacturing. GSCM practices focus on reducing the environmental impact of SC activities [80]. BDAC can enable organizations to utilize data-driven insights to identify inefficiencies, reduce carbon footprints, waste, and hazardous substances in manufacturing, and adopt energy-efficient processes, thereby improving CE [79]. Thus, GSCM could play a potential mediating role in the relationship between BDA and CE.

Therefore, we hypothesize the following:

H7. 

Green Supply Chain Management mediates the relationship between big data analytics and the circular economy.

The theoretical model is illustrated in Figure 1.

3. Methodology

3.1. Research Design

The primary objective of this study is to examine the impact of BDA on the CE with the mediating role of GSCMand SP. Adopting a quantitative research approach, the study employs a survey as the primary data collection instrument. The questionnaire is organized into four distinct structural categories and entails variables related to BDA, SP, GSCM, and CE.

3.2. Research Sample

This study employs a convenience sampling method, targeting a readily accessible sample of employees from pharmaceutical companies in Saudi Arabia. To enhance representativeness, efforts are made to include participants with diverse demographic backgrounds, such as age, gender, educational level, job position, and experience (see

Table 1. Summarizes the demographic analysis of the sample.

Demographic Data	Frequency	Percent
Gender
Male	153
Female	122
Total	275	100.0
Age
18—Less than 28 years	52
28—Less than 38 years	105
38—Less than 48 years	71
More than 48 years	47
Total	275	100.0
Number of Years Working with the Company
Less than 4 years	32
4 to less than 8 years	46
8 to less than 12 years	134
12 years and above	63
Total	275	100.0
Education
High School and less	65
Diploma	59
Bachelor’s degree	91
Higher studies	60
Total	275	100.0
Job Level in the Company
Senior Level	43
Middle Level	67
Junior Level	165
Total	275	100.0
Type of Contract
Permanent	235
Temporary	24
Other	16
Total	275	100.0

).

A sample consisting of 310 respondents was selected according to these specific characteristics. A total of 275 valid questionnaires were retrieved for statistical analysis, while 35 were excluded as invalid. The research was conducted between July 2024 and September 2024.

4. Data Assessment

Using SmartPLS_4, structural equation modelling was employed to test our model.

Measurement Model

The measurement model assessment validates constructs in a reliable and valid manner through various tests, including factor loadings, composite reliability (CR), and average variance extracted (AVE) [81]. The criteria are met with α coefficients exceeding 0.70, CR values above 0.70, and AVEs greater than 0.50, indicating strong convergent validity [82]. The results demonstrate high convergent validity, with CR values ranging from 0.938 to 0.949, well above the recommended threshold. AVEs, which ranged from 0.717 to 0.790, also meet the criteria, confirming the assessment of variation within indicators related to measurement error. All constructs showed Cronbach’s alpha values over 0.7, reflecting strong internal consistency. Summary findings are provided in

Table 2. Results of the measurement model.

Construct	Construct Items	Questionnaire Questions	Item Loading	Cronbach’s Alpha (α)	CR	AVE
Big Data Analytics [7,15,29]	BDA-1	Our dashboards provide us with the ability to delve into information to support root cause analysis and continuous improvement	0.901	0.933	0.949	0.790
	BDA-2	The managers in our company understand the importance of big data and its analytics in enhancing the value of our business	0.877
	BDA-3	In our company, the importance of big data and its analytics enhances the value of our business	0.923
	BDA-4	Our company uses advanced analytical techniques to enhance decision-making	0.901
	BDA-5	Our company provides appropriate training for employees to use big data analytics tools	0.839
Sustainable Performance [83]	SP-1	The company has an exceptional performance in environmental protection compared to other companies in the same field	0.922	0.925	0.943	0.770
	SP-2	The company frequently discusses environmental protection results internally	0.873
	SP-3	We have improved health and safety within the communities where we operate	0.893
	SP-4	The company strives to significantly enhance its green image	0.904
	SP-5	The company aims to achieve high employee satisfaction	0.790
Green Supply Chain Management [59,84]	GSCM-1	The company implements green purchasing practices	0.807	0.923	0.940	0.723
	GSCM-2	The company implements cross-functional collaboration practices to improve environmental performance	0.834
	GSCM-3	The company applies green supply chain information systems	0.801
	GSCM-4	The company implements green distribution practices	0.896
	GSCM-5	The company utilizes reverse logistics systems	0.884
	GSCM-6	The company has programs for environmental review and complaint management	0.876
Circular Economy [15,30]	CE-1	The company aims to reduce the consumption of hazardous materials	0.874	0.920	0.938	0.717
	CE-2	There is collaboration between different departments or functional areas in improving the company’s environmental practices	0.861
	CE-3	The company seeks to eliminate solid waste	0.803
	CE-4	There is a training program for employees and staff on environmental issues	0.913
	CE-5	Our company is committed to processes that reduce raw material and energy consumption	0.885
	CE-6	The company strives to use product packaging materials repeatedly	0.733

and Figure 2. Discriminant validity is confirmed using the Fornell–Larcker criterion, which compares construct AVEs with their correlations with other variables as illustrated in

Table 3. Discriminant validity (Fornell–Larcker criterion).

Construct	BDA	CE	GSCM	SP
BDA	0.889
CE	0.636	0.847
GSCM	0.554	0.712	0.850
SP	0.488	0.702	0.786	0.877

.

Discriminant validity is further evaluated using the heterotrait–monotrait ratio (HTMT), as suggested by Dijkstra and Henseler [85]. All HTMT ratios are below the strictest threshold of 0.85, indicating robust discriminant validity as shown in

Table 4. Heterotrait–monotrait ratio (HTMT).

Construct	BDA	CE	GSCM	SP
BDA	-----
CE	0.738	------
GSCM	0.566	0.712	------
SP	0.738	0.609	0.741	------

. All constructs passed discriminant validity via Fornell–Larcker and HTMT. Predictive relevance (Q²) and effect sizes (f²) reinforce the structural strength of the model. Therefore, discriminant validity analyses confirm that our constructs are sufficiently distinct, thus mitigating potential concerns regarding the jingle-jangle fallacy despite slightly elevated CR values [82].

The tests recommended by Peng and Lai [86] were conducted to assess the strength and quality of the structural model estimate, and all results were satisfactory.

Table 5. R², communality, and redundancy.

Construct	R2 adj	Q2	f2 (SP)	f2 (GSCM)	f2 (CE)
BDA	----------	0.702	0.619	0.566	0.438
SP	0.471	0.445	----	----	0.114
GSCM	0.605	0.863	----	----	0.154
CE	0.805	0.633	-----	----	--------

presents the outcomes of Stone–Geisser’s Q², which all surpassed the threshold of 0, as well as the relative effect sizes (f²) of the BDA, SP, and CE constructs, along with the R² values. The SRMR value, which is below 0.08, confirmed the reliability of the study model, in line with Dijkstra and Henseler [85]. Model fit confirmed via SRMR < 0.08, validating construct relationships.

The analysis employs the PLS algorithm and bootstrapping to evaluate the significance of path coefficients and their alignment with the expected directions, to test the hypotheses. The empirical results from the bootstrapping procedure, including β values, standard deviation values, t-values, and p-values for both direct (BDA → CE, BDA → SP, BDA → GSCM, SP → CE, and GSCM → CE) and indirect relationships (BDA → SP → CE; BDA → GSCM → CE) are summarized in

Table 6. Hypothesis testing.

Hypothesis	Path	Β	t-Value	p-Value	Decision
H1	BDA → CE	0.478	6.786	0.000	Supported
H2	BDA → SP	0.692	10.927	0.000	Supported
H3	BDA → GSCM	0.781	14.989	0.000	Supported
H4	SP → CE	0.214	2.460	0.016	Supported
H5	GSCM → CE	0.287	3.721	0.000	Supported
H6	BDA → SP → CE (Mediation)	0.148	2.537	0.013	Supported
H7	BDA → GSCM → CE (Mediation)	0.225	3.780	0.000	Supported

.

In line with the recommendation by ref. [87], specific indirect effects are estimated rather than total indirect effects when analyzing models with multiple mediators.

5. Results and Discussion

The results of our statistical analysis, presented in Table 6, confirm our hypotheses and reveal significant insights into theory and practice. The empirical analysis supports H1, H2, and H3. Confirming respectively that BDA exerts a strong direct positive impact on the CE (β = 0.478, t = 6.786, p < 0.001), SP (β = 0.692, t = 10.927, p < 0.001), as well as GSCM (β = 0.781, t = 14.989, p < 0.001). These results affirm the theoretical assumption that big data capabilities can directly enhance sustainable outcomes and operational efficiencies within the industrial sector. The findings corroborate the argument by ref. [88] that data-driven decision-making can accelerate the transition toward sustainable production and consumption, directly supporting SDG 12. Conversely, our findings diverge from traditional views, which suggest that CE practices primarily depend on regulatory pressure, highlighting instead the strategic role of digital capabilities in driving sustainable outcomes.

This study empirically validates a comprehensive model linking BDA to CE implementation in the pharmaceutical sector, offering a novel contribution by demonstrating how data capabilities can be effectively translated into sustainable practices within a highly regulated and resource-intensive industry.

Additionally, the results reveal a significant relationship between SP and CE (β = 0.214, p < 0.001), supporting H4. In support of Hypothesis 5, the main effect of GSCM on CE was also significant (β = 0.287 p < 0.001).

Moreover, the mediation analysis reveals that both SP and GSCM significantly mediate the relationship between BDA and the circular economy. Specifically, BDA improves SP (indirect effect β = 0.148, t = 2.537, p = 0.013) and GSCM (indirect effect β = 0.225, t = 3.780, p < 0.001), which in turn positively influence the circular economy, thereby supporting H6 and H7. This dual-mediator model provides new insights beyond the straightforward direct relationships established in prior literature. It demonstrates that operationalizing GSCM is a more effective pathway for translating data analytics capabilities into circular economy outcomes than relying solely on broader sustainability improvements. This finding builds on the work of ref. [12] on dynamic capabilities by shedding light on the specific mechanisms through which these capabilities are enacted in the context of CE implementation. This highlights the importance of considering mediating variables when designing strategies for digital transformation and sustainability.

Our findings advance theoretical understanding by empirically validating a proposed theoretical model where BDA functions as a dynamic capability that enables the simultaneous development of sustainable performance and green supply chain management capabilities. The dual-mediation mechanism operates through complementary pathways: SP creates internal organizational readiness for circular practices through resource optimization and stakeholder alignment, while GSCM establishes external collaborative networks essential for closed-loop material flows.

The impact mechanism of this result reveals how SP mediates the relationship between BDA and CE practices. On the economic front, cost optimization driven by BDA frees up resources that can be reinvested in circular initiatives. Environmentally, improvements such as waste reduction align directly with core CE principles. Socially, stronger stakeholder engagement enhances the organization’s capacity to embrace circular transformation. Taken together, these dimensions illustrate why SP plays a pivotal mediating role—it establishes the foundational conditions necessary for the successful adoption of CE practices.

Furthermore, the underlying mechanism of this result elucidates how SP mediates the relationship between BDA and CE practices. The capabilities offered by BDA, including predictive analytics, real-time data monitoring, and demand forecasting, support the integration of environmentally sustainable practices across supply chain operations—namely procurement, production, distribution, and end-of-life management. BDA enhances the transparency, efficiency, and flexibility of supply chains, thereby helping the firms develop the ability to deal with supply chain complexities and uncertainties while promoting sustainable outcomes [18,26,45].

BDA provides valuable insights that enable better decision-making and strategic planning, allowing organizations to focus on supply chain activities aimed at developing green suppliers, using environmental technology, and reducing waste, emissions, and energy consumption [79,80]. These sustainable environmental processes incorporate the principles of CE, which drive the implementation of CE practices.

These results align with previous research that integrates diverse indicators to provide a comprehensive assessment of circular economy (CE) performance. Particularly, the micro level—focusing on products, components, and materials—the Material Circularity Indicator is widely used to evaluate circularity. At the meso level, which encompasses businesses and industrial symbiosis, sustainable circularity indices are employed to measure aspects such as resource efficiency and collaborative practices within industrial ecosystems [70].

The study reveals a prominent attention, from the research sample, towards the impact of large-scale analysis of industrial sustainability and green interpersonal resource management in upholding the circular economy.

The results demonstrate that applying BDA techniques improves economic and environmental sustainability in pharmaceutical companies.

The study stresses the significance of developing and implementing strategies that leverage BDA to enhance sustainability and promote sustainable growth.

These results are consistent with and extend previous studies [8,15,29], which highlighted big data analytics positive influence on the circular economy. Some studies, such as ref. [10], also confirm that big data enhances environmental capability and operational performance.

The positive impact of BDA on the circular economy in the Saudi pharmaceutical sector can be explained at three main levels [89]. At the operational level, big data allows for the identification of inefficiencies in resource use throughout the pharmaceutical value chain. For example, predictive algorithms anticipate failures in production, thus reducing the waste of costly and dangerous raw materials. At the strategic level, big data analytics helps with evidence-based decision-making regarding investments in green technologies and circular infrastructures. This allows pharmaceutical companies to prioritize the most economically and ecologically profitable initiatives. Finally, at the supply chain level, big data improves the visibility and traceability of material flows, facilitating proactive management and the identification of industrial symbiosis opportunities, essential in a sector subject to strict regulations.

These impacts at different levels align with the transformative service perspective recently advocated by ref. [71], who emphasized the importance of considering value retention and regeneration processes across organizational levels. Our results demonstrate that, in the pharmaceutical sector, BDA can enhance transformation processes at both operational and strategic levels, providing empirical validation for theoretical frameworks that have remained largely conceptual in the existing literature.

The findings highlight the vital role of SP and GSCM as mediators that reinforce the shift toward a regenerative economy. This positive influence proves big data’s capability to enhance efficiency and attain sustainability goals through developing GSCM approaches and upgrading operational strategies. SP plays a mediating role in the relationship between BDA and the CE by optimizing resource management and reducing the environmental footprint, thus creating a favorable environment for the adoption of circular economy practices. For its part, the green supply chain translates the impact of big data on supplier relationships and logistics practices, promoting the integration of closed loops specific to this model [89]. Our model advances the literature by empirically validating dual mediation effects of SP and GSCM in the BDA–CE relationship, applying state-of-the-art analytical tools (PLS-SEM, SRMR, Q², f²), and focusing on a high-impact emerging economy context.

Examining our results in light of existing literature, we find that the positive impact of BDA on the CE coincides with the conclusions of ref. [38]. Our results on the mediating role of SP between BDA and the CE confirm the observations of ref. [15], while extending their application to the specific context of the pharmaceutical sector in emerging markets. This mediation can be explained by the fact that BDA allows for the identification of opportunities for resource optimization and waste reduction, which in turn improve sustainable performance, ultimately leading to better implementation of circular economy practices.

Contrary to ref. [50], who did not identify a mediating role of environmental performance between BDA and industrial sustainability, our study reveals a significant mediating effect in the Saudi pharmaceutical context. This divergence can be explained by the specificities of the pharmaceutical sector, which is highly regulated and where the management of hazardous waste and product traceability are major concerns, thus making the use of big data particularly relevant for achieving sustainability and circular economic objectives.

6. Research Contributions and Practical Implications

Our study makes a significant contribution to data-driven sustainable transformation by empirically exploring the complex links between BDA, SP, GSCM, and CE implementation in the Saudi pharmaceutical sector. Building on previous research, we uniquely identify and validate two key mechanisms through which pharmaceutical companies can achieve their circular economy goals, thereby addressing calls for more context-specific studies in emerging markets increasingly facing distinctive sustainability challenges.

This study contributes to the theoretical understanding of circular economy capability development by proposing an integrated framework that draws on the RBV and DC perspective, where BDA functions as a dynamic capability that simultaneously enables the development of SP and GSCM for analyzing digital transformation in sustainability contexts. These theoretical lenses explain how BDA enables organizations to develop CE competencies through parallel capability-building pathways. By linking digital enablers with sustainability-oriented capabilities, this framework advances theory at the intersection of digital transformation and CE research.

Our theoretical contribution goes beyond previous studies by demonstrating that dual mediation pathways function as complementary rather than competing mechanisms. The internal pathway, mediated by SP, strengthens the organizational foundation through resource optimization and stakeholder engagement. In parallel, the external pathway, mediated by GSCM, fosters collaborative networks that are critical for enabling circular material flows. This complementarity accounts for the persistence of both mediation effects and helps reconcile inconsistencies reported in earlier single-mediator studies.

This study has significant implications for the Sustainable Development Goals (SDGs), particularly SDG 12 (Responsible Consumption and Production) and SDG 9 (Industry, Innovation, and Infrastructure). The empirical evidence shows that the strategic application of BDA within CE-driven supply chains can improve operational transparency, reduce waste, and foster sustainable resource management. These practices directly align with SDG 12, which advocates sustainable consumption patterns. Similarly, the integration of digital tools and innovative analytics into supply chain processes supports SDG 9 by promoting sustainable industrialization and fostering innovation. By highlighting the mechanisms through which data-driven strategies contribute to these global goals, the study provides actionable insights for practitioners, policymakers, and scholars aiming to align business practices with international sustainability targets.

Our findings provide guidance to decision-makers in line with Saudi Arabia’s Vision 2030, recommending targeted investments in digital infrastructure, strengthening analytical capabilities, and implementing specialized training programs. Companies should prioritize ecological monitoring of the supply chain through big data, identify and address resource inefficiencies throughout their operations, and develop integrated expertise in technical management and sustainability to successfully transition to a CE.

We put forward a significant number of recommendations:

• Integrate Large-Scale Data Analysis in the Pharmaceutical Sector: Apply large-scale data analysis procedures to develop resource efficiency and mitigate waste, thus enhancing industrial sustainability and reinforcing the shift toward a CE.
• Promote Sustainability into Corporate Strategies: Utilize sustainability as a core component of long-term strategies by updating big data to enhance operations, develop green supply chain management, and mitigate environmental impacts.
• Support Green Supply Chain Management: Facilitate policies that encourage green supply system monitoring in accordance with large-scale data analysis with the aim of increasing productivity, efficiency, and sustainability.
• Construct Public–Private Collaboration to Improve the Circular Economy: Support cooperation between governments and businesses to promote the circular economy through legislation and policies that encourage big data analytics for better recycling processes and waste reduction.
• Offer Training Programs on Sustainability and Big Data: Implement training programs to increase awareness amongst businesses and employees about the importance of industrial sustainability and the CE, and to construct expertise in BDA.
• Advance Technological Infrastructure for Big Data Application: Enhance digital infrastructure within companies to reinforce the adoption of large-scale data analysis systems, namely, investments in modern information systems.
• Support Innovation in Sustainability Using Big Data: Encourage innovation in sustainability through big data, encouraging the development of new solutions to upgrade industrial performance and mitigate environmental influence.
• Tackle Global Developments in Big Data and Sustainability: Track the latest studies and developments in AI and BDA and discover the ways in which they can reinforce economic and environmental sustainability for optimal outcomes.
• Policy Recommendations for Sustainable Practices: For policymakers, the study suggests improving a regulatory framework to encourage sustainable practices within the industrial sector and providing incentives for companies investing in green technologies and circular economic applications. Encouraging public–private partnerships to promote knowledge and improvements in sustainable technologies is also critical.

7. Conclusions, Limitations, and Future Research

This study has revealed how BDA can serve as a catalyst to promote the CE in the pharmaceutical sector in Saudi Arabia, with significant implications for the achievement of Vision 2030 objectives. Our results confirm that BDA positively influences the CE, both directly and indirectly, through SP and GSCM. The integrated framework developed in this study constitutes a strategic guide for pharmaceutical companies wishing to leverage digital technologies to achieve their sustainability objectives.

Despite its contributions, our study has several limitations. First, the cross-sectional nature of our research limits our ability to observe the dynamic evolution of the relationships studied over time. Future research could adopt a longitudinal approach to examine how the relationships between BDA and the CE evolve during the different phases of digital maturity of companies.

Second, to strengthen the validity of the results, future research should use different statistical analysis methods, such as CB-SEM models, as well as segmentation and non-linear modeling approaches to explore more complex relationships and identify hidden heterogeneities in the sample.

Third, future research could further refine and empirically validate the proposed framework by more explicitly operationalizing RBV and DC constructs in the context of circular economy transitions.

Finally, our sample is limited to the Saudi pharmaceutical sector, which could limit the generalizability of our results to other sectoral or geographical contexts. Although the Saudi pharmaceutical industry shares structural and operational characteristics with other resource- and innovation-intensive industries, such as chemicals and biotechnology, and the issues explored, particularly digital transformation and sustainable practices- transcend sectoral and geographical boundaries, we encourage future research in other sectors and regions to confirm and enrich the scope of our model. Future research could replicate our model in various national and sectoral contexts to assess its robustness and examine moderating variables related to socio-economic, cultural, or regulatory factors, such as regulatory pressure or competitive intensity.

Comparative studies between sectors or countries would also provide valuable insight into the influence of contextual factors on the effectiveness of BDA initiatives in favor of the CE.