Environment Sustainability Is a Corporate Social Responsibility: Measuring the Nexus between Sustainable Supply Chain Management, Big Data Analytics Capabilities, and Organizational Performance

Abstract

Sustainability has profound implications for environmental competitiveness, yet little has been done to study the feasibility of sustainable supply chain management (SSCM) practices as a predictor of organizational performance (operational and environmental performance). By integrating stakeholder theory and dynamic capability theory, this study aims to determine the impact of corporate social responsibility (CSR) on SSCM practices and assess its impact on organizational performance. This research also investigates the role of big data analytical capabilities (BDAC) in mediating the relationship between SSCM practices and organizational performance. The authors collected data online, examined 320 valid responses, and tested research hypotheses. The findings suggest that CSR (both internal and external CSR) positively promotes SSCM practices and contributes to expanding dynamic capacity theory in the context of BDA capabilities. BDAC is also a key mediator between SSCM practices and organizational performance. These results contribute to and improve the research on stakeholder theory and SSCM practice and provide a new perspective for scholars to further study this issue.

1. Introduction

Businesses are increasingly concerned about environmental protection and achieving sustainable development goals (SDGs) has become a global priority, owing to their critical significance in economic development and well-being [1]. Interest in SGDs has resulted in a significant increase in investment in research in enterprises that face shareholder pressure to implement sustainable development policies [2,3,4]. Sustainable supply chain management (SSCM) attained academic and practitioner attention in achieving SDGs. There is a focus on waste generation, ecosystem disruption, and natural resource depletion. If an organization’s actions result in irrevocable harm to the ecosystem and fail to ensure safety, security, a living wage, healthcare, better employee working environment, and an improved standard of living for the neighboring community and society at large, there is a question mark over the organization’s sustainable performance [5]. This trend has triggered academics and practitioners to pursue SSCM. SSCM has become an essential way for organizations to achieve SDGs and boost organizational performance [6,7,8].

SSCM is referred to as “the management of material and information flows, as well as enterprise interaction, along the supply chain (SC) while taking into account all important components of sustainable development” [9]. In contrast, many global SCs have been demonstrated to be opaque and prone to potential sustainability violations from a sustainability aspect [10]. They argue that even if those breaches were not sufficient in and of themselves, they would still be sufficient to warrant a reevaluation of current SC practices, which negatively influence sustainable organizational performance. Traditional supply chain management (SCM) is unable to provide a suitable response to current stakeholder needs, not only because of its adverse environmental effects but also because of widespread public awareness of environmental issues in all areas of the world, including those that are difficult to reach [11,12,13]. The green ecological approach and its emphasis on sustainability thus become an alternative method of administering public requests to regulate resource consumption in SC [14,15]. The study of Das (2017) conceptualized, developed, and validated a scale to measure the SSCM practices followed by a company and evaluated the organization’s performance on multiple dimensions of SSCM, among other things.

On the other hand, stakeholder theory gives a suitable theoretical viewpoint to understand stakeholder affiliations and has grown as the leading concept in the literature on corporate social responsibility (CSR) [16,17]. Similarly, an organization’s application of CSR activities does not stop at meeting the needs of its employees and the local community; it frequently results in a considerable improvement in SCM practices and performance [5,18]. However, we must provide answers to questions such as how SSCM occurs from the stakeholder theory perspectives (e.g., CSR); otherwise, the unknown nature of these difficulties would hamper our general knowledge of SSCM. Organizations considering adopting and implementing SSCM practices might benefit from the findings of a collaborative investigation of SSCM practices and outcomes from the perspective of CSR [19]. Additionally, the integration of information technology has significantly aided and shaped organizational development in all areas involving SCM and sustainable performance [5,20,21,22]. Prior studies illustrate the critical significance of resources, competencies, and skills in ensuring a company’s competitiveness and performance in current competitive settings [23]. Meanwhile, according to the dynamic capability theory, an organization’s competitiveness is determined by its capacity to utilize its capability. However, what type of capability can continue to provide value in a fast-changing environment [21]?

Simultaneously, the importance of big data analytics (BDA) in steering organizational decision-making has garnered considerable attention in recent years [24]. An increasing number of businesses are accelerating their BDA projects to gain essential insight that will finally offer them a competitive advantage and impact sustainable organizational performance [25,26,27,28]. According to the findings of [25], the versatility of the BDA infrastructure is defined by its connectivity, compatibility, and adaptability. Numerous studies indicate that the accumulation of data has prompted many businesses to build analytical tools such as BDA in order to translate the data into valuable information that may aid in decision making and SC performance [26,27,28,29].

SSCM practices combine CSR and green SCM goals to assist organizations in achieving their operational and environmental performance at the micro-level, and ultimately improve the organization’s image in the eyes of stakeholders to improve its sustainable performance. Although the researchers described the relationship, no attempt has been made empirically to put the framework into practice in a real-world scenario, particularly the impact of BDA capabilities. The current study aims to close this gap by identifying the management perception of the impact of CSR (e.g., internal and external CSR) on the development of SSCM practices, which leads to long-term organizational success while using BDA capabilities.

• To this end, this research aims to address and analyze the subsequent questions.
• How does CSR (internal and external) affect SSCM practices in developing countries?
• What effect do SSCM practices have on sustainable organizational performance?
• What role do BDACs play in mediating the relationship between SSCM practices and sustainable organizational performance?

Both a theoretical discussion of the mechanisms behind the relationship of SSCM and organizational performance and empirical findings that could aid in identifying the mechanisms that prevail in various settings and contexts are lacking in the literature. Thus, this research aims to close a knowledge gap by analyzing the possible influence of CSR on SSCM practices that improve organizational performance via BDACs.

The rest of this article is organized as follows. The second part reviews the relevant literature and analyzes the limitations of previous studies; in addition, the hypotheses and framework of this paper are proposed. Part three is the method of data collection and analysis, and part four is the analysis and detailed discussion of the collected data results. Finally, the fifth part summarizes the research results, puts forward the implications of this research, and points out the limitations and prospective.

2. Literature and Hypotheses

2.1. Corporate Social Responsibility

CSR is gaining adhesion in the business sector and management studies. Prior research on organizational perspectives on CSR has shown inconsistent findings of the impact of CSR on organizational performance [30,31,32]. Among the several definitions of CSR, [33] defines CSR as “a concept whereby companies integrate social and environmental concerns in their business operations and their interaction with their stakeholders voluntarily.” Furthermore, as per the stakeholder theory, CSR demonstrates an enterprise’s activity that benefits society beyond its immediate concerns and legal responsibilities [17,34]. In reality, CSR is a collection of business activities that go ahead of economic concerns and positively impact organizational stakeholders [35]. Beyond their own immediate interests, the interests of their shareholders, and legal responsibilities, CSR implies the measures performed by businesses to support or acquiesce to particular social causes [34,36]. As a result, CSR can be thought of as a mechanism for corporations to respond to their stakeholders that goes into enhancing SSCM practices.

CSR encompasses both internal and external CSR. Internal CSR refers to organizational practices that improve working conditions. On the other side, external CSR refers to “efforts that promote environmental preservation, community development, and sustainability” [35]. Organizations that engage in CSR (both internal and external CSR) are more likely to understand the demands of internal and external stakeholders, create value for the environment and society, and reduce the relapse of environmental challenges [17,37]. This, however, will facilitate environmentally friendly operations and the application of SSCM practices. Internal CSR refers to efforts to improve management practices toward employees, with employees as the primary beneficiaries of internal CSR initiatives [38]. By incorporating sustainable development activities into SCM, SSCM practices can assist organizations in reducing resource waste and increasing ecological efficiency throughout the SC [39,40]. Internal CSR and SSCM are inextricably intertwined. Internal CSR encourages employees to positively approach their employers, leading to employees optimizing business procedures [17]. As a result, organizations with internal CSR may positively transform existing SCM practices and foster the emergence of SSCM practices, among other things.

CSR efforts directed towards the management practices of external stakeholders are referred to as external CSR [38]. The companies that engage in external CSR activities and generate value for the environment and society can also consider them for strategic decision-making [41,42]. A study of [43] demonstrated that institutional and strategic perspectives of CSR can provide options for obtaining firm performance, which may add deeper insights in the context of emerging economies. A study of [44] empirically examined the impact of SSC practices on financial performance using Indian companies as an example and identified the potential for implementing environmental, social, and governance (ESG) practices to achieve organizational financial benefits and competitive advantage. Moreover, a comparative study [45] revealed that, in the advanced economies considered, CSR strategies are significant predictors of corporate ESG performance. Because it is intended to benefit external stakeholders, external CSR drives organizations to adopt SSCM practices as part of their overall strategy [38]. Organizations may benefit from external CSR, since it might help them rethink their previous attitudes and establish SSCM practices. Meanwhile, organizations with external CSR can face intense pressure from external stakeholders, which may enforce proper methods to meet the requirements of external stakeholders [17], laying the groundwork for implementing SSCM practices. Even though numerous investigations have analyzed the influence of CSR activities on environmental sustainability [37,38,46,47,48,49], an empirical investigation is required to explore the effect of CSR (internal and external CSR) activities on SSCM practices. Organizations with internal and external CSR are more likely to reassess their previous views about SCM, increasing the chance of implementing SSCM practices more effectively [17]. Therefore, in context to this study, we hypothesized the following.

Hypothesis 1a (H1a).

Internal CSR positively affects SSCM practices.

Hypothesis 1b (H1b).

External CSR positively affects SSCM practices.

2.2. SSCM Practices

Due to the dynamic and turbulent nature of the business environment, organizations must increase their profitability and sustainability in order to maintain a competitive edge [20]. In this case, SSCM is an essential component of the organizations that assist in getting high performance and the achievement of SDGs. The study of [50] defined SSCM as integrating SCM and sustainable development. A study of [51] concludes the definition of SSCM as a management process that integrates environmental factors, social performance, and economic contributions to different customer needs, and complex product components leading to intense internal and global competition among companies. Organizations that approach SSCM place a significant emphasis on decision making as a means of orienting themselves to success in the management of their SC [52]. The body of knowledge on SSCM continues to increase [53,54,55] and now encompasses critical areas such as greening suppliers, risk management, stakeholder alignment, information sharing, prioritizing, and cooperation [56]. The link between innovative technology (e.g., BDA) and organizational performance, on the other hand, is significantly less focused.

Since SCM has made considerable use of various technologies, including sensors, barcodes, and the Internet of Things to integrate and coordinate each link in the chain. Therefore, it is unsurprising that BDA has transformed SCs, and its application in SCM has been documented in several special issues [57,58]. It works as a bridge between SCM practices and organizational performance. Big data application in SCM has been discussed as SC data science and SC analytics, which are fundamentally identical with advanced qualitative and quantitative analytics for SCM objectives by exploiting the massive amount of fast-moving and diversified data [59]. As a result, it is possible to predict that SSCM practices may facilitate the establishment and execution of exclusive BDACs, enhancing organizational performance.

Numerous studies asserted that SCM is also meant to boost operational and environmental performance [20,52,60]. Additionally, SSCM practices can assist the organization in reducing environmental impacts and increasing sustainable activities by requiring them to address sustainable performance [61]. A study of [62] examined an SSCM in which carbon emissions are regulated using a cap-and-trade system. Apart from these, an early study in SSCM focused primarily on environmental management/green SCM practices espoused by an organization and their impact on performance. In contrast, subsequent research incorporates a social dimension into green SCM practices and examines the influence of various SSCM dimensions on organizational performance [5]. The absence of scholarly attention to the influence of SSCM practices on achieving sustainable organizational performance is the primary motivation for this study, especially when BDAC is integrated.

Following sustainability principles and the idea of SCM, the SSCM practices examined in this study are classified into three broad categories: environmental management practices, operations practices, and SCI. These categories are meant to encompass the essence of SSCM practices [63]. The rationale for considering environmental management practices (EMPs) is an important part of influencing organizational performance and implementing GSCM practices [5,62]. As a result, environmental factors significantly affect an organization’s overall performance and competitiveness. Operations practices (OPR) entail implementing operations management approaches to increase efficiency, enhance quality, lower inventory, and avoid waste throughout the value chain [5]. Supply chain integration (SCI) integrates upstream and downstream customers and numerous internal operations [64]. SCI has been suggested to integrate information in both directions between suppliers, manufacturers, distributors, and customers [65,66,67,68,69,70]. Based on the above discussion, we measured SSCM practices as a second-order formative construct consisting of EMP, OPR, and SCI to identify the impact on BDAC and organizational performance. Hence, we hypnotized the following.

Hypothesis 2a (H2a).

SSCM practices positively affect BDAC.

Hypothesis 2b (H2b).

SSCM practices positively affect OP.

Hypothesis 2c (H2c).

SSCM practices positively affect EP.

2.3. BDA Capabilities

Due to the huge transformative capabilities in business, management, and research, big data has been deemed a transformation in the manufacturing environment [71,72,73,74]. BDA is defined as the process of extracting meaningful knowledge from massive amounts of data through the application of advanced analytics tools, hence facilitating data-driven decision-making [57]. Meanwhile, the BDAC concept refers to “the ability of an organization to integrate, build, and reconfigure the information resources, as well as business processes, to address rapidly changing environments” [75]. Furthermore, BDAC is a multifaceted, complementary capability that helps firms to improve their current organizational models and value-added processes jointly by successfully coordinating and utilizing their data, technologies, and expertise [76,77]. The dynamic capability theory explains how an organization can achieve sustained performance in a rapidly changing industry by embracing continual change. Dynamic capabilities refer to “the capacity of an organization to create, extend, or modify its resource base purposefully” [78]. As such, BDAC is a dynamic capability that assembles, incorporates, and delivers resources optimized for big data [79]. Understanding BDAC and its efficient and effective utilization may improve an organization’s decision-making and sustainable performance.

The study of [80] stated that while the existing literature has numerous discussions on SCM’s predictive analytics and data science, it fails to demonstrate a direct link between SSCM practices and BDAC. Similarly, a few studies have indicated that BDAC has a beneficial effect on SSCM; however, these studies are rare in number and do not use the same set of measures [80]. As a result, generalizations about the relationship between BDAC and SSCM practices must be established. Indeed, several experts suggest that the effect of information technology on organizational performance may be mediated by a variety of intermediary variables [81,82]. BDAC is presently regarded as a game-changer to enable an organization’s sustainable performance due to its great operational and strategic potential. The emerging literature establishes a positive correlation between BDAC and organizational performance in various industries [24,25,80,83,84]. Consistent with previous research, we regard BDAC as a critical dynamic capability contributing to sustainable performance in the current study environment.

Therefore, this study analyzed the impact of BDAC on organizational performance and its mediating effect on the relationship of SSCM practices and organizational performance. As a result, this study postulated the following:

Hypothesis 3a (H3a).

BDAC positively affects OP.

Hypothesis 3b (H3b).

BDAC mediates the relationship between SSCM practices and OP.

Hypothesis 4a (H4a).

BDAC positively affects EP.

Hypothesis 4b (H4b).

BDAC mediates the relationship between SSCM practices and EP.

2.4. Sustainable Organizational Performance

Sustainable development is a contentious subject with widely varied viewpoints and attitudes [85]. The term is defined primarily as the intersection of the economy, the environment, and society. The concept of sustainable development has garnered considerable attention in recent decades, notably by the Brundtland report [11]. The role of business in sustainable development is often regarded as a “responsibility” to society, and that responsibility is defined as the need to eliminate the negative impact of business [86]. Organizational sustainability can be a source of competitiveness if opportunities related to sustainability can be identified appropriately. Although SSCM and logistics management are the most extensively researched topics within the sustainable development literature [87], there is a compelling academic need to determine whether organizational performance improves due to implementing SSCM practices.

This study considers two categories of organizational performance: environmental performance and operational performance. Operational performance is the ability of a manufacturing plant to supply and produce products more efficiently for its end consumers [88]. Operational performance is critical for businesses because it enables them to boost the efficacy of production processes and produce high-quality products, resulting in greater revenue and profit [89,90]. It refers to how an organization’s performance has improved in terms of cost reduction and increased efficiency across the whole SC. It refers to the core and most usually discussed competitive areas of flexibility, quality, pricing, and delivery, resulting in increased performance levels if attained by a corporation.

Meanwhile, environmental performance refers to manufacturers’ efforts to reduce solid waste, air emissions, effluent waste, and hazardous material use [91]. Fundamental sustainable activities, such as sustainable distribution, considerably improve manufacturing enterprises’ environmental performance [92,93]. Strong partnerships and associations with suppliers, particularly manufacturing, contribute to developing and implementing modern operational and environmental technologies [88]. Thus, operation performance is critical in enhancing an organization’s environmental performance. Implementing SSCM practices improves operational and environmental performance and increases supplier or customer retention. Therefore, we hypothesized:

Hypothesis 5 (H5).

OP positively affects EP.

Based on the above theoretical discussion, Figure 1 represents the proposed framework of this study.

3. Material and Methods

3.1. Selection of Context

Due to low taxation, cheap labor, and environmental regulations, automobile production has become an ideal choice for some developing countries [94]. Manufacturing is a major source of industrial waste and a contributor to environmental degradation, posing a threat to the sustainability of the environment [95]. To determine the relationship between the study hypotheses, the researchers collected information from workers of automotive manufacturing in Pakistan who completed a survey questionnaire. The automotive sector in Pakistan is one of the country’s fastest-growing industries, adding about 2.8% to the country’s overall gross domestic product [96]. Sales facts for the nine months studied (from July 2020 to March 2021) were much better, with a 36% rise year on year [97]. This growth potentially contributes to environmental problems and the sustainable performance of the automotive sector in Pakistan. Let us look at why SC visibility is an essential component of both organizational success and SDGs. In this environment, sustainability is more than just a trendy phrase; it has the potential to decide prospective success or failure as rules and contests become more stringent. Therefore, the target population of this survey is based on the employees/managers of automotive manufacturers in Pakistan. The researchers specifically chose several automotive manufacturers in Pakistan to maximize the efficiency with which they collected data.

3.2. Operationalization of Constructs

As previously stated, the researchers conducted a systematic poll of the intended respondents to validate the proposed framework. The researchers utilized a seven-point Likert scale ranging from 1 to 7. The survey items were adopted from prior research and adjusted to fit this study’s viewpoint (see Appendix A). Two scholars were asked to examine the pre-questionnaire and evaluate its rationality to determine the questionnaire’s content validity. After finalizing the questionnaire, it was circulated for data collection (the Appendix A contains the questionnaire). Additionally, the researchers quantified SSCM practices using a second-order formative scale centered on three primary variables (EMP, OPR, and SCI).

3.3. Data Collection, Sampling, and Analysis Techniques

The data collecting procedure was as follows, based on the study’s emphasis. First, senior executives from randomly selected automotive manufacturing organizations in Pakistan were called and informed about the study’s goal. Following permission from the organization, the researchers sent the questionnaire to top management and requested that it be circulated to appropriate managers. Given that the data was obtained between May and August of 2021, due to the COVID-19 epidemic, the Computer-Assisted Web Interview (CAWI) technique was most appropriate for data gathering [98]. To get reliable information on the specified dimensions, the researchers chose managers from each organization who were familiar with these activities as key information providers (CSR and SSCM practices). The researchers received 329 questionnaire replies as of May 2021. Of these, nine were excluded from data analysis due to a lack of consent or unengaged responses. Thus, after deleting these responses, the final evaluation included 320 final responses. According to the literature, a sample size of 200 or more is appropriate when using a structural equation model (SEM) [99], which the researchers met.

Additionally, this study assessed the sample size’s appropriateness using Cohen’s power theory. The authors used the post-hoc test in the G*Power software to confirm the statistical strength of the gathered sample for all exogenous factors, including formative indicators. The significance threshold was set at 0.05, the effect size was 0.15, and the sample size was 320. The post-doc analysis demonstrated that the statistical strength is significantly more than the 0.8 criteria [100]. PLS-SEM analysis was utilized to determine the association between the specified constructs using the Smart-PLS v3 software, as previously proposed in prior works [101,102]. The formative construct of SSCM practices incorporated a two-stage repeated indicator approach [103,104] for calculating the structural model’s outputs.

4. Results and Discussion

4.1. Respondent’s Profile

Table 1. Respondent’s profile.

Category		Frequency	Percent
Gender	Male	236	73.8
	Female	84	26.3
	Total	320	100.0
Work Experience	1–3 years	82	25.6
	4–6 years	119	37.2
	7–9 years	42	13.1
	10 years or above	77	24.1
	Total	320	100.0
Education	Undergraduate	46	14.4
	Graduate	113	35.3
	Postgraduate	90	28.1
	Other (Professional education)	71	22.2
	Total	320	100.0

summarizes the respondents’ demographic characteristics (gender, experience, and education). In total, 73.8 percent of respondents were male, while 26.3 percent were female. The findings indicate that 25.6 percent of respondents had between one and three years of job experience, 37.2 percent had between four and six years of work experience, and 37.2 percent had more than six years of work experience. A total of 14.4 percent of respondents held an undergraduate degree, 35.3 percent held a graduate degree, and 28.1 percent held a postgraduate degree. Only 22.2% of respondents reported having additional degrees or professional education. We can assume from these findings that our respondents are well educated and experienced enough to comprehend the questionnaire.

4.2. The Measurement Model

4.2.1. Reliability and Convergent Validity

Convergent validity describes the degree to which distinct indicators within the same structure are correlated [101]. This study employed Smart-PLS v3 to perform confirmatory factor analysis (CFA) on each item to determine its convergent validity. The reliability and convergence validity analyses for this investigation are summarized in

Table 2. Reliability and convergent validity.

Constructs	Cronbach’s Alpha	rho_A	CR	AVE
BDAC	0.938	0.940	0.949	0.698
ECSR	0.933	0.934	0.946	0.715
EMP	0.936	0.937	0.949	0.757
EP	0.916	0.917	0.937	0.747
ICSR	0.919	0.926	0.939	0.754
OP	0.949	0.955	0.957	0.737
OPR	0.923	0.925	0.942	0.764
SCI	0.890	0.892	0.924	0.752

BDAC = big data analytics capabilities; ECSR = external corporate social responsibility; EMP = environmental management practices; EP = environmental performance; ICSR = inter corporate social responsibility; OP = operational performance; OPR = operations practices; SCI = supply chain integration.

. Cronbach’s alpha values for all constructs were between 0.890 and 0.949, above the suggested level. According to the threshold values, the composite reliability (CR) ranged from 0.924–0.957, and the average variance extracted (AVE) was 0.698–0.764. As a result, the findings in this research demonstrate that there are no issues with convergence validity or reliability.

4.2.2. Discriminant Validity

Discriminant validity is defined as “the extent to which variables differ empirically” [101]. Three approaches were used in this study to assess discriminant validity. First, the researchers related each factor’s association to the square root of AVE. Second, they assessed the relevance of the survey items using item loadings and cross-loadings. Thirdly, they determined the heterotrait–monotrait ratio (HTMT) [101,105,106].

As illustrated in

Table 3. Fornell–Larcker criterion.

Constructs	BDAC	ECSR	EMP	EP	ICSR	OP	OPR	SCI
BDAC	0.835
ECSR	0.535	0.846
EMP	0.493	0.457	0.870
EP	0.466	0.480	0.453	0.865
ICSR	0.442	0.487	0.378	0.390	0.868
OP	0.503	0.447	0.486	0.460	0.429	0.859
OPR	0.418	0.496	0.491	0.430	0.447	0.440	0.874
SCI	0.466	0.489	0.553	0.495	0.440	0.448	0.455	0.867

Note: Diagonal bold-faced values are the square root of the average variance extracted from each construct. Pearson correlations are shown below the diagonals, p < 0.05. BDAC = big data analytics capabilities; ECSR = external corporate social responsibility; EMP = environmental management practices; EP = environmental performance; ICSR = inter corporate social responsibility; OP = operational performance; OPR = operations practices; SCI = supply chain integration.

, the link between the constructs and the square root of AVE was used to determine the instrument’s validity, a criterion known as the Fornell–Larcker criterion. In Table 3, the diagonal values suggest that the square root of AVE is greater than the correlation coefficient between the variables. The findings indicate no discriminant validity problem [107].

Prior research has used cross-loading criteria to determine discriminant validity [20,106,108]. According to the literature, each entry’s load should be greater than the burden of the succeeding construct, and the entry load is also regarded as a threshold. The item loads and cross-loads for all linked values are shown in

Table 4. Construct’s cross-loadings.

Constructs	BDAC	ECSR	EMP	EP	ICSR	OP	OPR	SCI
BDAC1	0.785	0.524	0.431	0.392	0.364	0.373	0.368	0.367
BDAC2	0.882	0.443	0.385	0.415	0.367	0.456	0.395	0.376
BDAC3	0.869	0.465	0.428	0.419	0.365	0.442	0.373	0.423
BDAC4	0.815	0.416	0.416	0.366	0.367	0.402	0.299	0.394
BDAC5	0.898	0.479	0.439	0.439	0.384	0.467	0.367	0.435
BDAC6	0.862	0.435	0.396	0.375	0.377	0.393	0.355	0.369
BDAC7	0.770	0.398	0.383	0.359	0.378	0.388	0.351	0.372
BDAC8	0.792	0.412	0.413	0.339	0.355	0.430	0.276	0.372
ECSR1	0.462	0.789	0.380	0.404	0.413	0.405	0.401	0.390
ECSR2	0.463	0.874	0.383	0.373	0.406	0.403	0.431	0.367
ECSR3	0.459	0.842	0.364	0.423	0.436	0.372	0.420	0.396
ECSR4	0.456	0.865	0.417	0.412	0.432	0.392	0.422	0.460
ECSR5	0.441	0.828	0.373	0.382	0.374	0.374	0.402	0.410
ECSR6	0.430	0.855	0.402	0.421	0.404	0.375	0.429	0.428
ECSR7	0.458	0.865	0.384	0.425	0.415	0.329	0.429	0.439
EMP1	0.438	0.411	0.813	0.361	0.380	0.435	0.400	0.427
EMP2	0.429	0.404	0.910	0.399	0.322	0.444	0.430	0.498
EMP3	0.390	0.312	0.843	0.395	0.285	0.426	0.407	0.461
EMP4	0.450	0.406	0.888	0.387	0.316	0.399	0.394	0.511
EMP5	0.420	0.420	0.893	0.401	0.318	0.415	0.479	0.477
EMP6	0.447	0.430	0.871	0.421	0.356	0.419	0.450	0.511
EP1	0.403	0.414	0.373	0.846	0.352	0.405	0.346	0.407
EP2	0.389	0.405	0.387	0.863	0.313	0.358	0.361	0.406
EP3	0.377	0.377	0.372	0.865	0.332	0.406	0.335	0.417
EP4	0.413	0.418	0.373	0.893	0.320	0.403	0.360	0.455
EP5	0.428	0.454	0.446	0.855	0.365	0.412	0.448	0.450
ICSR1	0.415	0.488	0.367	0.342	0.876	0.410	0.434	0.450
ICSR2	0.394	0.466	0.355	0.350	0.880	0.384	0.415	0.381
ICSR3	0.385	0.379	0.292	0.354	0.879	0.337	0.366	0.356
ICSR4	0.341	0.371	0.275	0.283	0.847	0.304	0.319	0.308
ICSR5	0.375	0.387	0.334	0.356	0.859	0.408	0.385	0.394
OP1	0.415	0.351	0.335	0.369	0.337	0.839	0.306	0.381
OP2	0.419	0.414	0.466	0.415	0.346	0.886	0.421	0.380
OP3	0.491	0.421	0.466	0.390	0.400	0.867	0.366	0.395
OP4	0.422	0.331	0.420	0.381	0.347	0.846	0.384	0.378
OP5	0.320	0.226	0.272	0.247	0.297	0.822	0.230	0.293
OP6	0.459	0.488	0.480	0.469	0.463	0.857	0.418	0.477
OP7	0.488	0.436	0.473	0.450	0.346	0.884	0.411	0.397
OP8	0.392	0.327	0.351	0.374	0.381	0.867	0.428	0.338
OPR1	0.371	0.421	0.407	0.430	0.411	0.383	0.833	0.397
OPR2	0.347	0.421	0.435	0.342	0.391	0.377	0.891	0.380
OPR3	0.302	0.375	0.376	0.327	0.365	0.393	0.859	0.323
OPR4	0.382	0.463	0.434	0.367	0.388	0.374	0.876	0.423
OPR5	0.415	0.478	0.486	0.411	0.396	0.396	0.910	0.458
SCI1	0.470	0.483	0.533	0.425	0.402	0.409	0.440	0.878
SCI2	0.321	0.354	0.433	0.384	0.353	0.308	0.370	0.863
SCI3	0.367	0.423	0.459	0.451	0.350	0.397	0.373	0.873
SCI4	0.447	0.428	0.488	0.455	0.419	0.436	0.392	0.854

Note: All factor loadings are significant at the p < 0.001 level. Bold-faced values are the factor loadings.

, demonstrating that the factor item loads are more significant than those of other potential factors. This implies that the distinction is sufficiently valid to satisfy the cross-loading criterion.

Finally, the ratio of HTMT is near to one, indicating a lack of discriminant validity in path testing [106]. The HTMT approximates the correlation between numerous variables (more precisely, the upper boundary). Henseler et al. (2016) advised that HTMT values should be less than one. As a result, we also use the HTMT ratio; as shown in

Table 5. HTMT ratio criterion.

Constructs	BDAC	ECSR	EMP	EP	ICSR	OP	OPR
ECSR	0.573
EMP	0.527	0.488
EP	0.501	0.518	0.488
ICSR	0.475	0.520	0.404	0.422
OP	0.525	0.463	0.505	0.482	0.450
OPR	0.447	0.532	0.526	0.465	0.480	0.461
SCI	0.507	0.533	0.604	0.546	0.480	0.479	0.498

BDAC = big data analytics capabilities; ECSR = external corporate social responsibility; EMP = environmental management practices; EP = environmental performance; ICSR = inter corporate social responsibility; OP = operational performance; OPR = operations practices; SCI = supply chain integration.

, the highest value is 0.604, which is less than the suggested limit, indicating that discriminant validity is sufficient.

4.3. Method Bias and Multicollinearity

The researchers utilized SPSS v26 software to conduct Harman’s single factor test to determine whether this study had a common method bias (CMB) issue [109,110,111]. The scores signaled that the first factor explained 39% of the variation, less than the 50% requirement for CMB [110,112]. Simultaneously, the inner variance inflation factor (VIF) was employed in Smart-PLS v3 to identify any CMB issues. Kock (2015) states that this number should not exceed 3.3. These values vary between 1.00 and 1.728, demonstrating that CMB was not an issue in this investigation. Meanwhile, the outer VIF was employed to test for multicollinearity. The highest VIF value found in this study was 4.82, which is under the suggested threshold value of 10 [113,114]. As a result, no evidence of significant multicollinearity was discovered.

4.4. The Structural Model

After confirming the model’s reliability and validity, Smart-PLS v3 was used to quantify the hypothetical connectivity along the standardized path [101,109]. Compared with covariance-based SEM, this software is better suited for dealing with potential paths of formative and reflected [104,115]. It is a powerful tool for measuring indirect relationships of path models using PLS-SEM [103]. The beta coefficients are depicted in Figure 2. The SEM path’s significance level was determined using bootstrapping, with a total of 5000 resampling’s. The suggested model’s descriptive capacity can be quantified by the explanatory deviation of the results (i.e., the R-squared value).

SSCMP’s adjusted R-squared value was 0.404, indicating that the internal and external CSR accounted for 40.4 percent of the variation in the SSCM practices. Meanwhile, the R-square for BDAC was 0.317, indicating that SSCM practices accounted for 31.7 percent of BDAC variation. OP’s R-squared value was 0.367, while EP’s R-squared value was 0.361, showing the factors’ effective participation.

As per the SEM results in Figure 2, all exogenous constructs in this investigation are positively linked with endogenous structures. The values of the beta coefficients for bootstrapping, the significance of the direct impacts, and path analysis are all included in

Table 6. Results for testing hypotheses.

Hypotheses	Original Sample (O)	Sample Mean (M)	S. D.	T Statistics (|O/STDEV|)	p Values
H1a = ICSR → SSCMP	0.293	0.292	0.047	6.282	0.000
H1b = ECSR → SSCMP	0.439	0.436	0.054	8.184	0.000
H2a = SSCMP → BDAC	0.563	0.561	0.058	9.761	0.000
H2b = SSCMP → OP	0.409	0.408	0.055	7.497	0.000
H2c = SSCMP → EP	0.362	0.360	0.070	5.188	0.000
H3a = BDAC → OP	0.272	0.270	0.053	5.113	0.000
H3b = SSCMP → BDAC → OP	0.153	0.152	0.035	4.431	0.000
H4a = BDAC → EP	0.178	0.179	0.057	3.122	0.002
H4b = SSCMP > BDAC → EP	0.100	0.100	0.033	3.004	0.003
H5 = OP > EP	0.166	0.163	0.061	2.727	0.006

ICSR = inter corporate social responsibility; ECSR = external corporate social responsibility; SSCMP = sustainable supply chain management practices; BDAC = big data analytics capabilities; OP = operational performance; EP = environmental performance.

. The results suggest that the t-statistic value is greater than the recommended value of 1.96, indicating the existence of a meaningful association between the quasi-variables [102,103]. Additionally, the p-value is included to indicate its significance.

As shown in Table 6, the SEM analysis confirms that the path’s analysis coefficient between ICSR and SSCM practices is 0.293, which is statistically significant at the 0.000 level. These findings indicate that internal CSR has a positive effect on SSCM practices. The beta coefficients between ECSR and SSCM practices are 0.439 (p = 0.000), suggesting that external CSR strongly affects SSCM practices. Based on these findings, we may conclude that organizational CSR activities, whether internal or external, significantly and positively contribute to developing SSCM practices (as a second-order construct) in automotive organizations. Therefore, H1a and H1b are statistically supported based on these findings.

The SSCM practices’ beta coefficient also demonstrated a strong positive effect on BDAC. In the path analysis, the measurement value was 0.563 (p = 0.000). As a result, H2a is supported. SSCM practices were linked to OP and EP positively. The beta coefficient demonstrates a statistically significant and positive association between SSCM practices and OP (b = 0.409, p = 0.000) and between SSCM practices and EP (b = 0.362, p = 0.000). As a result, H2b and H2c are approved.

Additionally, the findings indicate that BDAC significantly impacts the automotive organization’s OP and EP. According to Table 6, the association between BDAC and OP is 0.272 (p = 0.000), whereas the relationship between BDAC and EP is 0.178 (p = 0.002). This indicates that BDAC has a considerable impact on an organization’s sustainable performance. As a result, H3a and H4a are likewise endorsed.

Additionally, this study examined the mediating effect of BDAC on the connection between SSCM practices and sustained organizational performance. The results suggest that BDAC mediated the association between SSCM practices and sustainable organizational performance positively and significantly. However, the direct association between SSCM practices and sustainable organizational performance (OP and EP) remains strong—a partial mediation—thus, H3b and H4b are supported. Furthermore, the results indicate that improving an organization’s operational performance can positively affect its environmental performance. The correlation between OP and EP was determined as 0.166, with a significance level of 0.006.

Overall results show that all proposed hypotheses are supported and statistically significant because the t-statistics and p-values meet the threshold. We can conclude that the proposed model is statistically significant and acceptable from the results.

5. Conclusions, Implications, and Future Directions

5.1. Conclusions

This study investigated the influence of CSR, namely internal and external CSR on SSCM practices, which eventually results in achieving sustainable organizational performance (OP and EP) using its BDACs. SSCM practices were measured using second-order formatting constructs (EMP, OPR, and SCI). For this objective, the authors performed an empirical analysis using data acquired from Pakistani automotive manufacturing managers through a survey questionnaire to evaluate the proposed hypotheses.

Overall, the findings of this study suggest that CSR (both internal and external CSR) has a positive impact on SSCM practices in automotive manufacturing organizations. The findings show that internal or external CSR activities can improve organizational SSCM practices. To improve SSCM, organizations should establish and improve their internal CSR and external CSR activities. These findings are also consistent with the prior studies [17,116]. Meanwhile, as described in the second research question of this study, the most pressing problem we encounter is identifying the influence of SSCM practices on organizational performance, and the outcomes prove that SSCM practices have both direct and indirect impacts on sustainable organizational performance. These findings are also supported by the previous studies of [14,117].

The advancement of information technology and sensor technology has made it possible to collect large amounts of data from each SC partner, which has been beneficial in reducing the lack of knowledge about sustainability in SCs and improving the overall performance of organizations [80,118]. Therefore, according to the findings of this study, BDAC positively mediates the association between SSCM practices and organizational performance (OP and EP). It can be projected to empower managers to manage their SCs more effectively while improving organizational performance.

5.2. Theoretical Implications

This work adds to the intellectual property of the SSCM and SDG literature by forming prior theoretical and empirical investigations on SCM. First, this study extends stakeholder theory in SSCM by probing the association between two types of CSR (internal and external) and SSCM practices. While previous research has concentrated on reactive responses to stakeholder pressure in various fields, including SC [17,39,43,119], this study applied stakeholder theory to the field of SSCM to uncover previously unknown conclusions. The empirical outcome of the proposed framework reveals that this research contributes to expanding the notion of SSCM practices. Thus, we anticipated that internal and external CSR would significantly enhance organizational SSCM practices, ultimately assisting in achieving sustainable performance. These findings may aid in advancing our understanding of the link between CSR and SSCM.

Second, this study empirically explores and inflates the literature on the link between SSCM practices and sustainable organizational performance using BDAC, adding to the body of knowledge on this subject. Several prior researchers have demonstrated that BDACs play an increasing role in decision-making for sustainable development and performance [17,25,63,120]. Thus, this study adds to the body of knowledge on SSCM by empirically examining the mediating influence of BDAC on the link between SSCM and sustainable organizational performance.

Finally, this study examined SSCM practices from three distinct perspectives—EMP, OPR, and SCI—and established their significance in ensuring the sustainable performance of automotive manufacturing organizations (one of the largest manufacturing sectors). It may add a new dimension to established theoretical concepts of SSCM and sustainable performance, which can be employed in a wide range of diverse contexts.

5.3. Practical Implications

On the practical level, this study has the following implications. First, this study describes how CSR activities in manufacturing companies increase SSCM practice. Internal and external CSR initiatives encourage firms to adopt SSCM techniques to accomplish SDGs and improve organizational performance because organizations are responsible for business and social actions within their own premises and practices outside their own premises. Therefore, organizational policymakers should emphasize internal and external CSR initiatives that build a force to become involved in SSCM practices.

Second, our research demonstrates that organizations can reap the benefits of BDAC to improve their overall performance further. BDAC could play a constructive role in assisting policymakers and researchers design and implement policies, strategies, and practices that enable organizations to achieve both operational and environmental performance. This conceptual paper advises that organizations, managers, and entrepreneurs align their CSR activities, SSCM practices, and BDAC strategies to achieve sustainability in organizational performance.

Third, manufacturing organizations, especially in developing countries such as Pakistan, need to be aware of the areas of internal and external social responsibility related to achieving SSCM goals. Furthermore, it turns out that organizations need to develop an IT infrastructure capable of handling BDA for ultimate organizational performance.

Finally, this study recommends that policymakers reconsider the country’s environmental policies, as the manufacturing sector is seen as operating at a loss due to poor observable sustainability practices. To encourage organizations to implement environmental policy guidelines, policymakers must improve access to SSCM practices and technological development by building environmental practices for active learning programs, providing financial support, and incentivizing collaboration with customers and SC partners.

5.4. Limitations and Prospectives

The limitations of this study are few and point out the scope of further research. First, the scope of this study was confined to cross-sectional data from automotive manufacturing organizations collected at a single point in time. In the future, a longitudinal investigation will be conducted to better understand the sequence of relationships that exist between CSR, SSCM practices, BDAC, and sustainable organizational performance. Second, the data was gathered in Pakistan, where the working environment of automotive manufacturing organizations may be different from that seen in other nations. Therefore, data from a different geographic area should be used in the future to confirm the conclusions of this study. Finally, the model proposed in this study is a first step toward describing the link between CSR, SSCM practices, BDAC, and sustainable organizational performance. Future studies may incorporate additional relative factors such as external support, environmental instability, and market intensity to understand this concept.