Journal of Primeasia

1. Introduction

In a world grappling with climate change, natural resource depletion, and widening social inequities, environmental sustainability has shifted from peripheral concern to central strategic priority for businesses. Organizations are no longer judged solely by profitability; they are increasingly evaluated on their contributions to global Sustainable Development Goals (SDGs) and broader societal well‑being (Ilyas et al., 2020). The Sustainable Development Agenda 2030, with its emphasis on responsible consumption and production, climate action, and decent work, underlines the indispensable role of corporate actors in at least partial realization of global sustainability (Agbedahin, 2019; Álvarez Jaramillo et al., 2019). Yet this transition is neither linear nor simple—it requires fundamental shifts in how businesses manage operations, value chains, and strategic resources.

At the heart of this transformation lies Corporate Social Responsibility (CSR), which has evolved from discretionary philanthropy to a comprehensive framework for integrating social and environmental considerations into strategic decision‑making (Abeysekera & Fernando, 2020; Glavas, 2016). CSR is no longer limited to regulatory compliance; it reflects an organization’s active engagement with its stakeholders—employees, communities, customers, regulators, and the environment—affirming its role as a responsible global citizen (Wang, Zhang, & Zhang, 2020). In contexts where consumers and civil society are more informed and vocal than ever, corporations that fail to demonstrate responsible environmental behavior face reputational risk, regulatory scrutiny, and ultimately diminished competitive viability (Sebastianelli & Tamimi, 2020; Wissuwa & Durach, 2021).

Among the many strategic responses to this shift, Sustainable Supply Chain Management (SSCM) has risen to prominence as a comprehensive approach that aligns operational processes with sustainability objectives (Seuring, Sarkis, Müller, & Rao, 2008). SSCM extends traditional supply chain management by embedding ecological, social, and economic considerations across material, information, and financial flows (Das, 2017). Rather than simply optimizing efficiency or cost, SSCM frameworks emphasize waste reduction, ethical labor practices, conservation of ecosystems, and responsible sourcing throughout the supply network.

Empirical evidence from multi‑country and multi‑industry studies confirms that SSCM enhances both environmental performance and firm competitiveness, but the success of these practices is contingent on contextual and structural factors. In Malaysia, for example, SSCM practices have been shown to contribute significantly to organizational resilience and sustainability performance (Zailani et al., 2012). Similarly, integrative decision‑making frameworks developed within the Indian context highlight the practical challenges and potential pathways for organizations to incorporate sustainability without sacrificing operational goals (Rajesh, 2020). Such insights underscore the universality of SSCM principles alongside the importance of industry‑specific and regional nuances.

While SSCM’s theoretical foundations are well articulated, actual practices often vary widely, and progress remains inconsistent. Scholars have pointed to the continued opacity and complexity of global supply networks as a major hurdle in realizing the full sustainability potential of SSCM (Wissuwa & Durach, 2021). Traditional supply chains, by design, optimize flows for efficiency and cost, often overlooking or externalizing environmental and social impacts (Vachon & Mao, 2008). Moreover, capital constraints in developing economies exacerbate the challenges of adopting sustainability‑oriented innovations, requiring creative financing mechanisms such as partial credit guarantee contracts to stabilize operations (Yan, Sun, Zhang, & Liu, 2016).

Crucially, both internal and external drivers influence the implementation and effectiveness of SSCM practices. Internal CSR initiatives—such as enhancing employees’ working conditions and fostering a culture of sustainability—build foundational capacity for long‑term change (Glavas, 2016). These practices encourage workers to participate actively in green projects and enhance operational adaptability. External CSR efforts, directed toward environmental protection, community development, and transparent stakeholder engagement, compel organizations to rethink traditional, often opaque supply chain practices to align with societal expectations (Abeysekera & Fernando, 2020; Wang et al., 2020; Wissuwa & Durach, 2021).

Across the literature, three key SSCM dimensions emerge consistently: Environmental Management Practices (EMP)—activities aimed at reducing pollution, resource use, and ecological footprint; Operational Practices (OPR)—efforts to increase efficiency through lean production, waste reduction, and eco‑design; and Supply Chain Integration (SCI)—collaboration and information sharing across stakeholders to co‑create sustainability value (Das, 2017).

These practices, when implemented effectively, contribute not only to operational performance (e.g., cost savings, improved delivery reliability) but also to environmental performance (e.g., lower emissions, resource conservation), creating a reinforced strategic advantage (Shahzad et al., 2020).

However, achieving meaningful sustainability performance through SSCM is not possible without capable technological infrastructure. This is where Big Data Analytics Capabilities (BDAC) emerge as a critical mediator that connects CSR and SSCM practices with measurable organizational outcomes (Wamba et al., 2017; Wang et al., 2020). BDAC facilitates real‑time processing and interpretation of massive and complex data streams, enabling firms to make timely decisions that support environmental goals, risk management, and resource optimization.

Drawing on Dynamic Capability Theory, BDAC enhances a firm’s ability to reconfigure internal resources in response to rapid environmental changes (Barreto, 2010; Teece, Pisano, & Shuen, 1997). This ability is indispensable in contemporary supply chains marked by volatility, uncertainty, complexity, and ambiguity (VUCA). Research shows that BDAC improves predictive accuracy in sectors such as healthcare (Raghupathi & Raghupathi, 2014) and enhances firm performance when aligned with SSCM strategies (Mikalef, Boura, Lekakos, & Krogstie, 2019a; Mikalef et al., 2019b). Furthermore, stakeholder adoption behaviors toward environmental data platforms highlight the transformative potential of analytics in air‑pollution management (Shahbaz et al., 2021).

Yet BDAC implementation is not without challenges. Data quality issues, analytical talent shortages, and integration barriers can attenuate the potential benefits of big data in supply chain sustainability (Hazen, Boone, Ezell, & Jones‑Farmer, 2014; Arunachalam, Kumar, & Kawalek, 2018). Moreover, technology integration must be paired with organizational commitment and cultural readiness to translate analytic insights into operational change.

Finally, while direct technological and strategic mechanisms are essential, the broader psychological and organizational context significantly shapes sustainability outcomes. Productivity mediates the relationship between CSR and financial outcomes (Hasan, Kobeissi, Liu, & Wang, 2018), while organizational psychology perspectives indicate that employee perceptions and motivations influence the success of both CSR and SSCM initiatives (Glavas, 2016). Together, these insights remind us that sustainability is not merely a technical endeavor but a deeply human one.

2. Materials and Methods

2.1. Study Design and Systematic Review Approach

This study employed a systematic review and meta-analytic design to investigate the relationships between Corporate Social Responsibility (CSR), Sustainable Supply Chain Management Practices (SSCMP), Big Data Analytics Capabilities (BDAC), and organizational performance outcomes, including operational and environmental performance. Systematic review methodology enables the rigorous aggregation of existing empirical evidence, while meta-analysis allows the quantification of effect sizes and statistical testing of hypothesized relationships (Das, 2017; Mikalef et al., 2019a).

The review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, ensuring transparent identification, screening, eligibility assessment, and inclusion of relevant studies, as illustrated in Figure 1 (Ilyas et al., 2020). Peer-reviewed journal articles were considered, focusing on studies that investigated the impact of internal CSR (employee-centered initiatives) and external CSR (environmental and societal initiatives) on SSCMP, and the subsequent influence of BDAC on operational and environmental performance. Both quantitative and mixed-method studies were included to capture comprehensive evidence, and only studies published in English from 2010 to 2022 were considered to reflect contemporary practices in sustainability and digital transformation (Shahzad et al., 2020; Wamba et al., 2017).

This approach allowed for comparative analysis across contexts and regions, including developing economies such as Pakistan and India, as well as developed economies like Germany and China (Khan & Qianli, 2017; Wissuwa & Durach, 2021). The systematic review also facilitated the evaluation of mediating and moderating variables, such as organizational culture, collaborative capabilities, and technology readiness, which influence the effectiveness of CSR and SSCM practices (Samad et al., 2021; Wang et al., 2020).

2.2. Data Sources and Literature Search Strategy

A comprehensive literature search was conducted across multiple databases, including Scopus, Web of Science, PubMed, ScienceDirect, and Google Scholar. The search utilized a combination of keywords and MeSH terms, structured with Boolean operators to capture relevant

Figure 1. PRISMA Flow Diagram Illustrating the Systematic Identification, Screening, Eligibility Assessment, and Inclusion of Studies on Corporate Social Responsibility, Sustainable Supply Chain Management, and Big Data Analytics Capabilities. This diagram traces the number of records identified, screened, excluded, and ultimately retained at each stage of the review process. It documents how the initial pool of database records was narrowed down through duplicate removal, title/abstract screening, and full-text eligibility assessment. The final set of studies shown here formed the basis for both the qualitative synthesis and the quantitative meta-analysis reported in this paper.

studies. Keywords included: “Corporate Social Responsibility,” “Sustainable Supply Chain Management,” “Green Supply Chain Practices,” “Big Data Analytics Capabilities,” “Operational Performance,” “Environmental Performance,” and “Organizational Performance.” Variations and synonyms were included to ensure comprehensive coverage.

The screening process involved a three-stage approach. First, duplicates were removed using EndNote X9. Second, titles and abstracts were independently reviewed by two researchers to identify relevant studies. Third, full-text articles were assessed against pre-established inclusion and exclusion criteria. Inclusion criteria were: (a) studies providing empirical or quantitative evidence on CSR, SSCMP, or BDAC; (b) studies reporting sufficient statistical data, such as path coefficients, standard deviations, t-statistics, or p-values; (c) studies published in peer-reviewed journals. Exclusion criteria included non-empirical studies, conference abstracts without full data, and articles focusing solely on financial or legal aspects without sustainability or technology integration components (Zhu et al., 2016; Mikalef et al., 2019b).

The final dataset comprised empirical studies, including cross-sectional surveys, longitudinal analyses, and mixed-method approaches. Extracted data included sample size, study context, country, sector, measurement instruments, independent and dependent variables, and reported effect sizes (Table 1). Where necessary, authors were contacted to obtain missing statistical information, enhancing the completeness and reliability of the meta-analysis (Das, 2017; Shahbaz et al., 2021).

2.3. Data Extraction and Coding

Data extraction followed a structured coding procedure to ensure consistency and accuracy. Each study was reviewed independently by two trained researchers, and discrepancies were resolved through discussion. Extracted variables included hypotheses tested, CSR dimensions (internal and external), SSCM practices, BDAC measures, operational performance, environmental performance, sample size, path coefficients (β), standard deviations (S.D.), t-statistics, and p-values (see Table 1).

CSR was categorized into internal (employee well-being, training, career development) and external (community engagement, environmental initiatives) components, following previous operationalizations (Glavas, 2016; Wang et al., 2020). SSCM practices were coded according to three dimensions: Environmental Management Practices (EMP), Operations Practices (OPR), and Supply Chain Integration (SCI), which collectively capture organizational strategies to manage environmental impacts, optimize operations, and enhance collaborative networks (Das, 2017; Zhu et al., 2022). BDAC was coded to reflect firms’ ability to gather, process, and analyze large datasets to support decision-making and sustainability initiatives (Wamba et al., 2017; Benzidia et al., 2021).

To ensure comparability, all effect sizes were converted into standardized path coefficients (β) where necessary. Mediating effects of BDAC between SSCMP and performance outcomes were coded following Baron and Kenny’s approach to mediation analysis (Hazen et al., 2014; Arunachalam et al., 2018). Additionally, organizational context variables such as sector (manufacturing, healthcare, services), region, and technological adoption level were documented to assess potential heterogeneity in the meta-analytic models (Shahzad et al., 2020; Tiwari et al., 2018).

2.4. Statistical Analysis and Meta-Analytic Procedure

Quantitative data were analyzed using a random-effects meta-analytic model, appropriate for studies with diverse contexts and measurement scales (Mikalef et al., 2019a; Dubey et al., 2016). This model accounts for between-study heterogeneity and produces robust estimates of overall effect sizes for each hypothesized relationship. The primary analyses focused on: (a) the effects of internal and external CSR on SSCMP; (b) the effects of SSCMP on BDAC, operational performance, and environmental performance; and (c) the direct and mediated effects of BDAC on performance outcomes (Table 1).

Heterogeneity was assessed using Cochran’s Q test and I² statistics, where I² values above 50% indicated substantial heterogeneity (Zailani et al., 2012). Sensitivity analyses were conducted by removing one study at a time to assess the robustness of the overall estimates. Publication bias was examined through funnel plots and Egger’s regression test, ensuring the validity of the meta-analytic findings (Shahbaz et al., 2021). Mediating effects were tested using the bootstrapping method with 5000 resamples, consistent with contemporary best practices in meta-analysis and structural equation modeling (Hazen et al., 2014; Wamba et al., 2017). Statistical significance was evaluated at α = 0.05. Data processing and analyses were

Table 1: Summary of Hypothesized Relationships, Standardized Path Coefficients (β), Standard Deviations, T-Statistics, and P-Values for the Structural Model Linking CSR, SSCMP, BDAC, and Performance Outcomes. This table reports the meta-analytic results for all eight hypothesized paths (H1a–H5), including the direction and strength of each relationship, its statistical significance, and the corresponding source reference. It allows readers to assess at a glance which relationships (e.g., CSR→SSCMP, SSCMP→BDAC, BDAC→Performance) were empirically supported across the studies reviewed.

Table 2: Descriptive Statistics — Sample Mean, Standard Deviation, and Sample Size — for Each Relationship Component and Mediated Effect Examined in the Meta-Analysis. This table complements Table 1 by presenting the underlying sample-level statistics (mean effect, standard deviation, and total sample size N) that were aggregated across studies for each construct pairing, including the two mediated effects (H3b, H4b). It gives readers the raw statistical inputs behind the path coefficients reported in the structural model.

conducted using R (version 4.2.2) and the metaSEM package, which provides specialized functions for meta-analytic structural equation modeling.

The final meta-analytic model provides a comprehensive framework linking CSR, SSCMP, BDAC, and firm performance outcomes. This approach not only quantifies the magnitude of these relationships but also highlights the critical moderating and mediating roles of BDAC, organizational context, and SSCM practices in promoting sustainable and high-performing organizations (Shahzad et al., 2020; Zhu et al., 2022).

3. Results

3.1 Discussion of statistical analysis

The statistical analyses conducted in this study provide comprehensive insights into the relationships between Corporate Social Responsibility (CSR), Sustainable Supply Chain Management Practices (SSCMP), Big Data Analytics Capabilities (BDAC), and firm performance, including operational and environmental outcomes. Table 1 presents the descriptive statistics and effect sizes for the direct relationships tested in the structural model. The path coefficients and their significance levels reveal that both internal and external CSR significantly influence SSCMP, with β = 0.293 (p < 0.001) and β = 0.439 (p < 0.001), respectively. These findings align with previous research emphasizing CSR’s dual role in driving employee engagement and societal compliance while fostering sustainable supply chain behaviors (Glavas, 2016; Wang, Zhang, & Zhang, 2020). Internal CSR initiatives, which focus on workforce well-being, training, and empowerment, appear to create a foundation for operational change, enabling firms to implement sustainability measures more effectively. External CSR efforts, oriented toward environmental stewardship and stakeholder engagement, further reinforce organizational commitment to sustainable supply chain practices by creating reputational incentives and compliance pressures (Abeysekera & Fernando, 2020; Wissuwa & Durach, 2021).

The influence of SSCMP on BDAC and performance outcomes was equally robust. As presented in Table 1, SSCMP significantly predicts BDAC (β = 0.563, p < 0.001), operational performance (β = 0.409, p < 0.001), and environmental performance (β = 0.362, p < 0.001). This indicates that firms that actively integrate sustainability principles into supply chain operations are better positioned to leverage data analytics for enhanced decision-making. The strong link between SSCMP and BDAC underscores the notion that sustainable supply chains generate structured, high-quality data streams, which are essential for advanced analytics, predictive modeling, and real-time operational control (Wamba et al., 2017; Mikalef et al., 2019a). Operational and environmental improvements emerge not only from process optimization but also from the firm’s ability to respond to environmental and societal demands in a data-informed manner, bridging strategic goals with measurable outcomes (Shahzad et al., 2020).

Further, the mediating role of BDAC was examined through path analysis and bootstrapping procedures. Table 1 summarizes these findings, indicating that BDAC partially mediates the effect of SSCMP on operational (β = 0.272, p < 0.001) and environmental performance (β = 0.178, p = 0.002). This mediation effect is critical because it demonstrates that SSCMP alone does not fully explain performance improvements; rather, the integration of analytics capabilities enables firms to extract actionable insights from supply chain processes. The results suggest that firms capable of combining sustainable practices with robust analytics infrastructure achieve superior performance, confirming dynamic capability theory’s assertion that organizations must reconfigure resources to adapt to changing environments (Barreto, 2010; Teece, Pisano, & Shuen, 1997).

To further interpret these results, Figures 2 and 3 illustrate the forest plots and funnel plots generated during the meta-analysis. Figure 2 displays the effect sizes across studies for the direct relationships between CSR, SSCMP, and performance outcomes. The consistency of positive coefficients across multiple studies highlights the stability of these relationships and supports the robustness of the aggregated estimates. Notably, studies with larger sample sizes and lower standard errors tend to cluster near the overall effect size, demonstrating high precision in measurement and reliability of the reported findings. Figure 3, the funnel plot, assesses publication bias and confirms the absence of significant asymmetry, indicating that the included studies provide an unbiased representation of the existing literature (Zailani et al., 2012; Shahbaz et al., 2021).

Table 2 further complements this analysis by presenting precision and variance data for each study, enabling calculation of the standard errors used in both the funnel and forest plots. The low standard errors observed across several high-quality studies underscore the reliability of the extracted effect sizes and reduce the likelihood that results are driven by statistical noise. For instance, the study by Qiao et al. (2024) demonstrates both a high effect size (β = 0.752) and a minimal standard error (0.029), emphasizing the strong predictive value of digital transformation as a mediator in enhancing performance outcomes. Similarly, Cahyadi et al. (2022) report a substantial effect of BDAC on individual employee performance (β = 0.79, SE = 0.25, p < 0.05), reinforcing the broader conclusion that data-driven capabilities amplify the impact of sustainable practices.

Beyond the statistical interpretation, these results offer meaningful theoretical and practical implications. The dual pathways from CSR to SSCMP underscore that sustainability interventions must operate both internally and externally to optimize outcomes. Internal initiatives cultivate organizational culture, employee engagement, and process adherence, while external efforts align operations with stakeholder expectations and regulatory demands. The mediating influence of BDAC further emphasizes the transformative potential of technology: merely implementing sustainability initiatives is insufficient without the analytical capacity to monitor, evaluate, and adjust processes in real time (Wang et al., 2020; Mikalef et al., 2019b).

Additionally, the robust positive effects of SSCMP on both operational and environmental performance highlight a convergence between ecological responsibility and operational efficiency. Firms adopting integrated sustainability practices are not only reducing environmental impact but also improving reliability, cost-effectiveness, and resource utilization. This convergence challenges the traditional perception of a trade-off between environmental stewardship and business performance and provides empirical evidence that sustainability can serve as a source of competitive advantage (Das, 2017; Wissuwa & Durach, 2021).

The meta-analytic synthesis also underscores the importance of contextual and organizational factors. High heterogeneity in certain studies, as indicated by I² statistics, reflects sectoral differences, regional variations, and organizational readiness for technology adoption. Manufacturing sectors tend to show stronger SSCMP-BDAC linkages due to structured production data availability, whereas service sectors display more moderate effects, emphasizing the necessity of tailoring sustainability and analytics strategies to specific organizational contexts (Shahzad et al., 2020; Dubey et al., 2016).

Finally, the results suggest that future research should continue to explore interactive and moderating factors that may influence these relationships, including organizational culture, leadership commitment, and stakeholder engagement strategies. The integration of CSR, SSCMP, and BDAC offers a holistic framework for advancing sustainability while simultaneously achieving operational and environmental performance targets, providing a model that firms globally can emulate (Glavas, 2016; Wamba et al., 2017).

The combined evidence from Tables 1–3 and Figures 2–3 establishes a clear and empirically supported narrative: CSR serves as a critical antecedent for sustainable supply chain practices, SSCMP drives operational and environmental performance, and BDAC mediates and amplifies these effects. This interconnected model highlights the necessity of integrating ethical responsibility, operational efficiency, and technological capability for firms aiming to thrive in the modern, sustainability-conscious business environment. The results not only advance theoretical understanding but also provide actionable insights for managers seeking to implement data-driven sustainability strategies effectively.

3.2 Interpretation and discussion of the funnel and forest plots

The forest and funnel plots presented in this study provide critical insights into the consistency, reliability, and potential biases of the relationships among Corporate Social Responsibility (CSR), Sustainable Supply Chain Management Practices (SSCMP), Big Data Analytics Capabilities (BDAC), and organizational performance outcomes. Forest plots, as illustrated in Figure 2, display the effect sizes for each included study, allowing for visual assessment of both the magnitude and direction of the observed relationships across diverse contexts. The aggregated effect sizes, calculated using a random-effects meta-analytic model, reveal that CSR—both internal and external—exerts a substantial and statistically significant influence on SSCMP. Specifically, the majority of studies cluster around positive path coefficients, indicating that CSR initiatives consistently promote sustainable supply chain behaviors, regardless of regional or sectoral variations. This pattern confirms that internal CSR,

Figure 2. Structural Model Showing Direct Path Coefficients and Effect Sizes Linking CSR, SSCM Practices, Big Data Analytics Capabilities, and Organizational Performance. This path diagram visually maps the hypothesized relationships tested in the meta-analytic structural model (H1a–H5), showing the standardized beta coefficients between internal/external CSR, SSCMP, BDAC, and operational/environmental performance. Arrow thickness and coefficient values reflect the relative strength of each relationship. It provides a visual companion to the numerical results reported in Table 1.

Figure 3. Funnel Plot Assessing Publication Bias Across Studies Included in the Meta-Analysis of Path Coefficients. Each point represents an individual study's effect size plotted against its standard error or precision. Symmetrical distribution around the pooled effect size (the funnel's center line) suggests an absence of significant publication bias, while asymmetry would indicate that smaller or non-significant studies may be underrepresented. This plot supports the reliability and generalizability of the pooled path coefficients reported elsewhere in the paper.

emphasizing employee engagement and training, establishes a foundational culture for sustainability, while external CSR, focused on community and environmental initiatives, reinforces organizational alignment with societal expectations and regulatory pressures (Glavas, 2016; Wang, Zhang, & Zhang, 2020).

The forest plot also highlights the strong positive effects of SSCMP on BDAC and subsequent operational and environmental performance. The data points in Figure 2 demonstrate that SSCMP enhances firms’ capacity to collect, process, and analyze complex supply chain data, which in turn facilitates data-driven decision-making and improved performance outcomes (Wamba et al., 2017; Mikalef et al., 2019a). Notably, the path from SSCMP to operational performance shows consistently high effect sizes across studies, emphasizing that sustainable supply chain strategies do not merely serve environmental objectives but also contribute to efficiency gains, cost reductions, and reliability improvements. Similarly, SSCMP’s influence on environmental performance is significant and positive, reflecting the capacity of sustainable practices to reduce emissions, optimize resource utilization, and integrate ecological responsibility into organizational processes (Das, 2017; Wissuwa & Durach, 2021).

The forest plot also allows for the assessment of variability and precision across studies. Studies with narrower confidence intervals, such as those reported by Qiao et al. (2024) and Cahyadi et al. (2022), indicate higher reliability and precision of effect size estimates, whereas studies with wider intervals reflect greater uncertainty, likely due to smaller sample sizes or context-specific factors. This variation underscores the importance of considering organizational, industrial, and regional heterogeneity when interpreting the generalizability of SSCMP and BDAC effects. Despite these variations, the overall pattern supports the robustness of the proposed structural model and validates the hypothesized pathways among CSR, SSCMP, BDAC, and performance outcomes (Shahbaz et al., 2020; Zailani et al., 2012).

The funnel plot, depicted in Figure 3, provides a complementary perspective by evaluating potential publication bias and the symmetry of effect size distribution. In this analysis, the funnel plot demonstrates a relatively symmetric distribution of studies around the pooled effect size, indicating minimal publication bias. Smaller studies with higher standard errors are scattered near the base of the funnel, while larger studies with smaller standard errors converge toward the top, as expected in a well-conducted meta-analysis (Shahbaz et al., 2021; Mikalef et al., 2019b). The absence of asymmetry confirms that the meta-analytic estimates are not artificially inflated by selective reporting of positive findings. Moreover, the inclusion of studies with varying sample sizes and contexts strengthens the generalizability of the findings, ensuring that the reported effects of CSR and SSCMP on performance outcomes are robust across different organizational environments.

The combination of forest and funnel plots offers a nuanced understanding of both the magnitude and reliability of the observed relationships. The forest plot visualizes the strength and direction of effects, revealing the consistent positive role of CSR and SSCMP in shaping BDAC and organizational performance. Concurrently, the funnel plot validates the meta-analytic approach by confirming that the observed effects are not unduly influenced by publication bias, thus reinforcing confidence in the aggregated effect sizes. Together, these plots provide compelling evidence that CSR initiatives, when coupled with sustainable supply chain practices and supported by data analytics capabilities, produce tangible improvements in operational efficiency and environmental outcomes.

Additionally, the plots highlight important methodological considerations for future research. The slight dispersion of certain studies with wider confidence intervals suggests that context-specific factors, such as industry type, regional regulatory frameworks, and technological readiness, may moderate the effectiveness of CSR and SSCMP. This observation aligns with prior research emphasizing that the benefits of sustainability-oriented initiatives are contingent on organizational capacity and environmental conditions (Dubey et al., 2016; Arunachalam, Kumar, & Kawalek, 2018). Thus, while the aggregated effect sizes indicate robust positive relationships, practitioners should consider the specific operational context when designing CSR and SSCMP interventions.

Finally, the visual synthesis provided by the forest and funnel plots underscores the mediating role of BDAC in translating sustainability initiatives into measurable performance improvements. Studies positioned at the upper portion of the forest plot, representing higher-precision estimates, consistently show that organizations integrating BDAC into SSCMP achieve superior operational and environmental outcomes. This pattern validates theoretical models suggesting that analytics capabilities enable firms to convert sustainable supply chain strategies into actionable insights, driving both ecological responsibility and operational efficiency (Wang et al., 2020; Mikalef et al., 2019a).

 The combined interpretation of the forest and funnel plots confirms the robustness, reliability, and generalizability of the study’s findings. CSR, through its internal and external dimensions, positively influences SSCMP, which in turn enhances BDAC and leads to improved operational and environmental performance. The plots collectively demonstrate that these relationships are consistent across studies, minimally affected by publication bias, and supported by high-precision empirical evidence. This visual and statistical synthesis provides a strong foundation for theoretical and practical advancements, reinforcing the critical interplay of ethical responsibility, operational sustainability, and data-driven decision-making in contemporary organizational strategy.

4. Discussion

The findings of this study provide compelling evidence regarding the interplay between Corporate Social Responsibility (CSR), Sustainable Supply Chain Management Practices (SSCMP), Big Data Analytics Capabilities (BDAC), and firm performance, encompassing both operational and environmental outcomes. Table 1 illustrates the structural model path coefficients, highlighting the statistically significant relationships among these constructs. The results reinforce the critical role of CSR as an antecedent of SSCMP and further demonstrate how BDAC mediates and enhances the influence of SSCMP on organizational performance. These findings align with previous literature emphasizing that ethical, social, and environmental responsibilities extend beyond compliance, serving as catalysts for operational excellence and sustainability (Abeysekera & Fernando, 2020; Glavas, 2016).

Internal CSR initiatives, which focus on employee development, engagement, and well-being, were found to positively impact SSCMP (β = 0.293, p < 0.001). This supports the notion that employee-centered CSR strategies foster a culture of accountability and operational alignment, encouraging workforce participation in sustainability initiatives (Hasan, Kobeissi, Liu, & Wang, 2018). External CSR, encompassing community engagement, environmental protection, and societal initiatives, demonstrated an even stronger effect on SSCMP (β = 0.439, p < 0.001), indicating that firms’ outward-facing ethical commitments incentivize sustainable practices across the supply chain. These results corroborate prior studies emphasizing that CSR efforts directed toward external stakeholders create reputational and regulatory pressures that motivate firms to adopt comprehensive SSCM practices (Zhu, Liu, & Lai, 2016; Wissuwa & Durach, 2021).

SSCMP itself emerged as a critical determinant of both BDAC and performance outcomes. The path coefficients from SSCMP to BDAC (β = 0.563, p < 0.001), operational performance (β = 0.409, p < 0.001), and environmental performance (β = 0.362, p < 0.001) underscore that sustainable supply chain practices not only reduce ecological impact but also enhance operational efficiency and process reliability. The strong SSCMP–BDAC linkage supports the argument that sustainable practices generate structured, high-quality data streams, enabling firms to leverage advanced analytics for real-time monitoring, predictive modeling, and strategic decision-making (Arunachalam, Kumar, & Kawalek, 2018; Benzidia, Makaoui, & Bentahar, 2021). This finding is consistent with the dynamic capabilities perspective, which posits that organizations achieve competitive advantage by reconfiguring resources and integrating technological capabilities to address environmental uncertainty (Barreto, 2010; Teece, Pisano, & Shuen, 1997).

The mediating role of BDAC further emphasizes its transformative function in linking SSCMP to performance. As shown in Table 1, BDAC partially mediates the relationship between SSCMP and operational performance (β = 0.272, p < 0.001) as well as environmental performance (β = 0.178, p = 0.002). This indicates that the mere implementation of sustainable practices is insufficient for achieving maximum performance; rather, firms must possess the analytical infrastructure to extract actionable insights from their supply chain data (Hazen, Boone, Ezell, & Jones Farmer, 2014; Wamba et al., 2017). In practical terms, organizations that integrate BDAC into SSCMP can anticipate, monitor, and mitigate operational and environmental risks, transforming sustainability initiatives into measurable outcomes (Mikalef, Boura, Lekakos, & Krogstie, 2019a; Raghupathi & Raghupathi, 2014).

The results of this study also highlight the strategic importance of contextual factors. Heterogeneity in effect sizes across studies suggests that sectoral, regional, and organizational differences influence the effectiveness of CSR, SSCMP, and BDAC. For instance, manufacturing and industrial firms tend to demonstrate stronger SSCMP–BDAC relationships due to structured production data, whereas service-based firms exhibit more moderate effects, reflecting differences in operational complexity and data availability (Dubey, Gunasekaran, Childe, Wamba, & Papadopoulos, 2016; Tiwari, Wee, & Daryanto, 2018). Additionally, studies indicate that organizational readiness, technological adoption, and leadership commitment serve as moderators in the CSR–SSCMP–BDAC pathway (Shahzad, Du, Khan, Shahbaz, & Murad, 2020; Ilyas, Hu, & Wiwattanakornwong, 2020). These insights reinforce the notion that sustainable and data-driven strategies cannot be universally applied but must be tailored to the unique organizational context.

The convergence between SSCMP and firm performance is particularly noteworthy. Operational performance benefits from streamlined processes, optimized resource utilization, and improved efficiency, while environmental performance improves through emission reductions, resource conservation, and waste minimization (Das, 2017; Vachon & Mao, 2008). This dual benefit challenges traditional perceptions of a trade-off between sustainability and profitability and aligns with the growing literature that positions CSR and SSCMP as drivers of long-term competitive advantage (Agbedahin, 2019; Rajesh, 2020). By leveraging BDAC, organizations can translate sustainable practices into strategic insights, further enhancing performance outcomes and fostering a culture of continuous improvement (Arunachalam et al., 2018; Benzidia et al., 2021).

Furthermore, the study extends theoretical understanding by illustrating the integrative role of BDAC as a mediator. While prior research has highlighted the separate effects of CSR and SSCMP on performance, this study demonstrates that data analytics capabilities serve as the bridge converting sustainable practices into operational and environmental outcomes (Shahbaz, Gao, Zhai, Shahzad, & Khan, 2021; Wamba et al., 2017). This finding underscores the necessity of a holistic approach in which ethical responsibility, sustainable operations, and technological competence are strategically aligned to achieve measurable results (Mikalef, Boura, Lekakos, & Krogstie, 2019b; Wang, Zhang, & Zhang, 2020).

Finally, the empirical evidence supports practical implications for managers and policymakers. Firms should invest in both internal and external CSR initiatives to cultivate a culture conducive to sustainability. Additionally, embedding SSCMP into core operations provides a pathway to operational and environmental excellence. Crucially, integrating BDAC ensures that sustainability efforts are informed by actionable data, enhancing responsiveness, risk management, and overall firm performance (Sebastianelli & Tamimi, 2020; Soltany, Rostamzadeh, & Skrickij, 2018). Organizations that fail to develop analytical capabilities may underutilize the potential of CSR and SSCMP, limiting their impact on performance outcomes (Khan, Tao, Ahmad, Shafique, & Nawaz, 2020; Joardar & Sarkis, 2021).

In conclusion, this study demonstrates that CSR, SSCMP, and BDAC operate in a synergistic framework to enhance operational and environmental performance. Table 1 confirms that both internal and external CSR significantly predict SSCMP, which in turn drives BDAC and performance outcomes. BDAC partially mediates these relationships, highlighting the importance of data-driven decision-making in translating sustainability initiatives into tangible results. The findings advance both theoretical understanding and managerial practice, providing a robust framework for integrating ethical, sustainable, and technological strategies in contemporary organizations (Abeysekera & Fernando, 2020; Glavas, 2016; Wamba et al., 2017).

5. Limitations

Despite the robust findings, this study has several limitations that warrant consideration. First, the analysis relied primarily on published empirical studies, which may introduce publication bias despite funnel plot analyses suggesting minimal asymmetry. Second, the majority of included studies were cross-sectional in design, limiting the ability to draw definitive causal inferences between CSR, SSCMP, BDAC, and performance outcomes (Shahzad et al., 2020; Wamba et al., 2017). Third, although the meta-analysis incorporated studies from diverse geographic and industrial contexts, differences in measurement scales, regulatory environments, and organizational culture may have introduced heterogeneity that could not be fully controlled (Dubey et al., 2016; Mikalef et al., 2019a). Fourth, the study focused on internal and external CSR dimensions, potentially overlooking other CSR aspects, such as governance or stakeholder engagement strategies, which may also influence SSCMP and BDAC effectiveness (Abeysekera & Fernando, 2020; Glavas, 2016). Finally, while BDAC was identified as a key mediator, the study did not explore other potential mediators or moderators, such as organizational readiness, leadership, or technological infrastructure, which could further elucidate the mechanisms through which CSR and SSCMP impact firm performance. Future research should adopt longitudinal designs, expand CSR dimensions, and examine additional contextual and organizational factors to enhance the generalizability and depth of understanding.

6. Conclusion

This study demonstrates that Corporate Social Responsibility, through both internal and external dimensions, significantly influences Sustainable Supply Chain Management Practices, which in turn enhance organizational performance. The findings highlight the critical mediating role of Big Data Analytics Capabilities in transforming sustainable initiatives into measurable operational and environmental outcomes. Integrating CSR, SSCMP, and BDAC provides firms with a strategic framework to achieve efficiency, environmental stewardship, and competitive advantage, emphasizing the necessity of combining ethical responsibility, sustainability, and technological capabilities in modern business management.

Author Contributions

S.C.B. conceptualized the study and developed the review framework. S.C.B. and M.R.H. conducted the literature search, study selection, data extraction, quality assessment, and evidence synthesis. S.C.B. performed the systematic review, meta-analysis, interpreted the findings, and prepared the original manuscript. M.R.H. contributed to data validation, critical revision of the manuscript, and interpretation of the results. Both authors reviewed, edited, approved the final manuscript, and agreed to be accountable for all aspects of the work.

Acknowledgements

The authors sincerely acknowledge their respective institutions for providing academic support and access to scientific resources used during the preparation of this systematic review and meta-analysis. They also thank the researchers whose published studies formed the foundation of this evidence synthesis. No specific financial support was received for this work.

References