Sustainable Supply Chain Practices: An Empirical Investigation from the Manufacturing Industry

Abstract

Recently, firms have adopted sustainable supply chain practices (SSCPs) to reduce the social and environmental impacts in their supply chain network. The primary objective is to reduce adversarial negative impacts on the environment, particularly those stemming from consumption of energy and water. Embracing sustainable business practices is increasingly essential for organizations and in addressing pressing global challenges. Based on 439 valid responses obtained through cross-sectional research from Turkish manufacturing firms, and combining practice-based view and organizational information processing theory, this study examines the effect of SSCP on economic performance (EP). The mediating roles of social performance (SP) and environmental performance (ENP) were examined. The moderating role of Industry 4.0 was further explored with regard to the aforementioned links. The results revealed that SSCP has a positive effect on EP. SSCP has positive effects on SP and ENP. SP and ENP have positive effects on EP. The relationship between SSCP and EP is partially mediated by both SP and ENP. Further, it was discovered that in firms with a high level of Industry 4.0 technologies usage, the impact of SSCP on ENP was stronger, and in firms with high level of Industry 4.0, the impact of SSCP on EP was also stronger. The findings offer valuable insights for research, as well as for managers, supply chain practitioners, and policymakers, in developing an integrated strategic and effective approach that promotes sustainable operations by using improved understanding of Industry 4.0, which also yields better economic outcomes.

1. Introduction

The triple-line concept, introduced by Elkington [1], proposes that a firm should aim to strike a balance between economic and environmental protection and social stability while aiming for its own development [2]. Among the three sustainability dimensions, economic outcomes have consistently been and will remain a primary concern for firms [3]. Hence, the primary focus in the pursuit of sustainable supply chain management (SSCM) initiatives has been observed to be economic performance [4]. Additionally, SSCM is widely recognized as a crucial tool for both the industry and the environment. Based on this, recent studies have demonstrated that the adoption of sustainable supply chain practices (SSCPs) promotes environmental performance (ENP) [5,6,7]. The importance of SSCP is increasing due to escalating environmental, social, and economic challenges globally [8], and they are also essential for the supply chain [5]. To achieve sustainability, firms must prioritize environmental friendliness by minimizing pollution and waste. In this regard, a sustainable supply chain seeks to promote green design to facilitate sustainable manufacturing processes [9].

To be less harmful to the environment, firms are constantly seeking means to conserve resources and minimize negative environmental and social impacts [10]. However, empirical research addressing this issue is still in its early stages [10,11]. While emerging studies [12,13] have demonstrated that implementing SSCP can be beneficial for firms by promoting their overall performance, which leads to financial gains, limited research has been conducted regarding the implementation of SSCP from the perspective of the Turkish manufacturing industry. Numerous studies have been conducted on SSCM and its various elements, including green purchasing and green design. However, there is a limited number of studies that have specifically examined green marketing with those elements [14]. From this standpoint, green marketing as an element of SSCP with regard to triple bottom line are rarely studied [10]. While emerging studies have examined the impact of SSCP on sustainability [10], research offering insights on how this impact develops is relatively scarce. Specifically, empirical research on how SSCP translates to economic performance is far from being clearly understood. Hence, this study responds to a research call by [10] to explore the mechanisms through which SSCP can translate into economic performance.

Industry 4.0 represent the advent of the fourth industrial revolution, which is attracting major interest from decision-makers and business leaders around the world. The primary focus is to make manufacturing operations/systems autonomous, efficient and sustainable [15]. Since the advent of the first industrial revolution during the 18th century, there have been global efforts to increase manufacturing output using limited resources, while simultaneously mitigating adverse environmental and social impacts. The implications of Industry 4.0 on society, environment and economy are being increasingly recognized, with a growing focus on how the current technological advancement can contribute to sustainability in the long term [10].

Scholars have acknowledged the significance of Industry 4.0 in addressing environmental issues [7,16,17]. Industry 4.0, yet in its infancy, is expected to have an enormous impact on sustainability [7]. While the economic environmental and economic impacts of smart manufacturing have been investigated, not much is known regarding digital technologies in the context of Industry 4.0 as an organizational condition in relation to the environment and the economy. Specifically, a scan of literature revealed that no empirical studies have explored the potential moderating role of Industry 4.0 in relation to sustainability (i.e., social, environmental and economic aspects) simultaneously. To bridge this gap, we draw from the practice-based view (PBV) concept [18] and organizational information processing theory (OIPT) [19] as theoretical foundations for our study. The concept of PBV explains how SSCPs are integrated and impact a firm’s social, environmental and financial performance [20]. Additionally, environmental and operations management are recognized as processes that heavily on information [21]. OIPT argues that organizations function as open socioeconomic systems and can attain superior performance by enhancing their ability to process information and quality of information [19]. The improved capabilities to process information in the supply chain, in addition to adoption of green practices, can contribute to the development of sustainable manufacturing businesses. Thus, based on PBV and IPT, we examine how SSCPs are applied in relation to the triple bottom in the manufacturing industry in Turkey. Further, we explored the moderating role of Industry 4.0 on the relationships in our conceptual model (as illustrated in Figure 1).

Hence, this empirical research aims to shed lights on the following questions:

• How does SSCP influence economic performance?
• Does social and economic performance mediate the link between SSCP and economic performance?
• Does Industry 4.0 technologies play a moderating role on the relationship between SSCP and social, environmental, and economic performance?

This study is motivated by two well-established theories (PBV and IPT) and attempts to add relevant knowledge to the literature on SSCM. This study has three significant contributions that should be highlighted. The current study aims to add supporting empirical evidence regarding the influence of SSCPs on social and environmental performance, as well as their implications on an organization’s EP. Second, the study also aims to uncover the underlying mechanisms through which SSCPs translate into a firm’s economic performance. Third, sustainable supply chain applications in Industry 4.0 have substantial untapped potential [22]. Hence, strong evidence that SSCP improves firm performance is required [10]. Based on this, we aim to extend the current literature on sustainable supply chain applications in Industry 4.0 (in relation to triple bottom line, including social performance) by examining the conditions under which the proposed predictor of economic performance could be less or more effective. In line with this expectation, we examine the moderating role of Industry 4.0 technologies on the relationships in our conceptual research model (see Figure 1). This is essential because the relationship between Industry 4.0 and the three dimensions of sustainability has received limited attention [10,23].

The subsequent sections of the article are structured as follows. The next subsection provides a comprehensive review of the existing literature, focusing on the theoretical foundation and hypothesis development; Section 2 presents the methodology, research design and data collection, the results; Section 3 highlights the discussion and also the implications drawn from the study.

1.1. Theoretical Underpinning and Hypothesis Development

This section carefully examines choosing the suitable variables to define and establishing a conceptual foundation for the proposed research constructs. Thus, the conceptual framework of the current study starts by providing an explanation of the theories and engaging in a discussion of each construct and its relevant components.

Theoretical Background

The current study is motivated by two well-established theories: the practice-based view (PBV) and organizational information processing theory (OIPT). PBV can provide insights into how to effectively use green practices and technology in relation to SSCP to address performance outcomes [18]. PBV seeks to illustrate how implementation of sustainable strategies can promote firm performance outcomes [24]. PBV focuses on the application of SSCP and its influence on triple bottom line [20]. To comprehend discrepancies in performance, the PBV framework emphasizes the importance of practices.

According to OIPT, it is important to align information processing requirements with information processing capability to achieve the best performance-related outcomes [25]. In recent years, OIPT has been widely used in various fields: integration of technology [26], production control [27] and supply chain management [28].

Gherardi [29] demonstrated how organizations can attain sustainable competitive advantages through the use of SSCPs. SSCPs can be transferred and duplicated, and are a predictor in the practice-based view perspective: sustainable performance is the outcome construct [10]. OIPT proposes that Industry 4.0 tools should improve operational capabilities [27]. Therefore, to investigate the relationships in our integrated theoretical model, both PBV and OIPT are considered appropriate. By combining PBV and OIPT, we aim to provide new insights on how the adoption of SSCP promotes sustainable performance. Our study further examines Industry 4.0 (as a condition), which impacts the application of SSCP to promote sustainable performance.

1.2. Sustainable Supply Chain Practices

Sustainability has emerged as a paramount concern globally, including various topics such as air pollution, waste, global warming and the greenhouse effect [30]. Over the past five decades, approximately 60% of the ecosystems of the planet have experienced a decline in their overall condition. Furthermore, it has been anticipated that by 2050, the population of the world will consume natural resources at a rate three to six times higher than the present level. However, it has also been anticipated that in 2050, the world’s population may exceed 9 billion, thereby presenting additional challenges to natural resources [10]. Therefore, there is a need for initiatives that can be beneficial in addressing these concerns in the form of environmental protection [31].

The concept of SSCP involves taking into account environmental factors during the manufacturing process with the aim of minimizing harm to the environment [32]. The primary objective of an eco-friendly industrial process is to develop a systematic approach to minimizing waste and its impact on the environment, while also improving a firm‘s SP [2]. The SSCP framework evaluates different stages of a product’s life cycle to determine its potential for recycling, reconditioning, or reuse. These practices in SSCPs can have a beneficial impact on society [32].

Furthermore, SSCPs such as green design and green purchase have been reported to promote sustainability performance [33,34] (Supplementary Materials). Green marketing is an element of SSCP often neglected in the literature. Khan et al. [10] pointed at the paucity of research including green marketing in SSCP. Thus, in furthering the existing body of knowledge, our study will measure SSCP as a unified construct that consists of green purchase, green design and green marketing. The definitions of the key constructs in this study are presented in

Table 1. Key construct definitions.

Constructs	Definitions	Source
Sustainable supply chain practices (SSCPs)
Green purchase	Green purchasing is a practice that focuses on obtaining components and raw materials in order to produce products that exhibit reduced detrimental impact on environment and promote people’s well-being.	Setyaning et al. [35]
Green design	It reduces waste via practices such as recycling, upcycling, reuse, environmentally friendly raw material sources, logistical reversal activities, and environmentally oriented production.
Green marketing	It is the practice of promoting goods and services that are less harmful to the environment than those of competitors.	Dahlquist [36]
Industry 4.0	Industry 4.0, also known as smart manufacturing or smart production, involves the integration of production systems through real-time data exchange and flexible production to facilitate customized manufacturing. In the context of Industry 4.0, sophisticated digital technologies commonly discussed by scholars and practitioners are the IoT, cloud computing, and BDA.	De Sousa Jabbour et al. [37]
Environmental performance	Describes the potential of SC to decrease hazardous waste use and promote the growth of plants with reduced greenhouse gases.	Abduloh [38]
Social performance	It refers to an organization’s ability to achieve its social goals and benefits, as well as the facets of its operations that impact the welfare, safety and development of people it serves.	Wood [39]
Economic performance	A firm’s economic performance relies on its capacity to generate profit, which is primarily achieved via the development of new products and the efficient utilization of resources.	Kenneth et al. [40]

.

1.3. Industry 4.0

The origin of Industry 4.0 can be traced back to Germany, where it was first introduced at the Hanover Trade Fair in 2011. Since then, it has gradually gained popularity and expanded to other countries. Within the framework of Industry 4.0, various aspects of operation and SCM can leverage big data analytic technology. These technologies have proven to be advantageous in several areas, including the examination of risks in the supply chain [41] and the advancement of social and environmental sustainability [42].

The Internet of Things (IoT) is another advanced technology in Industry 4.0. The implementation of the IoT in operation and SCM has been an area of interest among researchers. Various aspects of this application have been explored, including its use in production system [43], new ways of delivering products [44], and end-of-life recycling of products [45].

Cloud computing enables manufacturing firms to easily and cost-effectively access a wide range of personalized information from cloud service providers through the web without the need for complex and on-premise systems. According to Schniederjans and Hales [46], cloud computing is defined by its ability to scale resources massively and virtualize them, resulting in reduced requirements for support infrastructure. Additionally, it enables quick information dissemination, which offers potential for green computing. Cloud computing is anticipated to have beneficial effects on the growth and progress of manufacturing firms within the manufacturing community.

1.3.1. SSCP and EP

The implementation of SSCP has been reported to improve production efficiency [47], resulting in better economic achievement. Lai et al. [48] suggested that the implementation of green practices resulted in increased energy efficiency and improved economic effectiveness. Further, the literature suggests that many businesses are aware of the connection between SSCM practices, cost minimization and profit generation [47,49]. However, regarding SSCP, there are various practices that can adopted to integrate sustainability into internal businesses, such as green purchasing, eco-design and environmental certification [10,47,50]. From this perspective, previous studies have suggested that environmental credentials such as eco-design and ISO certificates contribute to organizations gaining competitive advantage and expanding their market share, while also improving their EP [51].

Additionally, the application of green practices, specifically the adoption of green marketing, can significantly enhance competitive advantage and improve economic performance. According to Rehman Khan and Yu [52], incorporating sustainable practices into business operations can enhance a company’s ability to attract customers and explore untapped market opportunities. Based on the above discussion, it is posited that:

H1: 

SSCP has a positive effect on economic performance.

1.3.2. SSCP, Social Performance and Environmental Performance

Dias-Sardinha and Reijnders [53] suggested that SSCP may promote firms’ social and environmental performance, which in turn can result in financial benefits. The development of SSCP practices has led to the integration of eco-friendly techniques in the development of goods, production, and distribution. Based on this, Mardani et al. [2] suggested that adopting such practices will result in the enhancement of social responsibility, profitability and economic viability. Additionally, the integration of an SSC with a focus on social responsibility is a crucial element within the sustainability framework of a firm. The extent to which a company prioritizes social responsibility has an important bearing on its success in the long run. As such, for an organization to achieve success, it is important that its supply chain operates in an environmentally and socially responsible manner.

According to Zhu and Sarkis [3], one aspect of SSCP involves the implementation of green purchase for raw material and the adoption of green design principles for products. These practices have been linked to firms’ environmental performance [54]. The evaluation of an organization’s EP involves considering various factors [26], including prevention of pollution and the impact of product or raw material on pollution, efforts to reduce the consumption of natural resources, initiatives to minimize fuel usage and the emission of greenhouse gases, the use of biodegradable products, and compliance with environmental regulations set by the country of operation. Further, SSCPs such as the adoption of green purchasing and green design practices, hold the potential to curtail environmental pollution and yield lasting benefits for a firm’s EP. Similarly, Jagani and Hong [55] pointed out that the adoption of multiple environmental practices is indicative of an intensified dedication to the cause and can yield major impacts on an organization’s environmental performance. Based on the above discussion, the following are posited:

H2: 

SSCP has a positive effect on SP.

H3: 

SSCP has a positive effect on ENP.

1.3.3. Social, Environmental and Economic Performance

Adopting a stringent environmental practice can boost a firm’s social and economic performance [10]. According to Govindan et al. [56], the adoption of eco-friendly production techniques may enable a firm to enhance its market share in situations where competitors encounter challenges in adhering to or matching the firm’s green standards. Our study builds upon previous research [10,57] by focusing on SP, ENP and EP. Investing in sustainable resource use and environmental improvement can be beneficial to a firm’s financial sustainability. Earlier research suggested that there is a mutual and strong rewarding relationship between being environmentally meticulous and achieving financial success for firms. It has been observed that a majority of the firms in China are adopting social and environmental responsible business practices as a means of enhancing their profitability [58].

Furthermore, environmentally responsible supply chain operations such as increased energy efficiency have been linked to a firms’ potential to generate profit [48,58]. Based on this, it is argued that manufacturing firms’ economic performance can be enhanced when they invest in measures that promote their social performance and improve resource efficiency. Thus, the following are posited:

H4: 

SP has a positive effect on EP.

H5: 

ENP has a positive effect on EP.

1.3.4. SP as a Mediator

SCM is crucial for the functioning and growth of businesses, and incorporating sustainability into SCM can facilitate the accomplishment of sustainable long-term operations [10,48,49]. By observing the debate on firms’ green practices to enhance performance [47,49], SSCP can be used to improve social performance. From this standpoint, firms have recognized the importance of engaging in social welfare activities as a means to uphold their social standing in the market [59].

Extant research has shown that there is a positive relationship between firms’ green practices and economic performance [10]. However, insignificant and mixed relationships have also been reported [60]. Several studies have even reported a negative relationship [61]. There exist numerous potential explanations for the observed inconsistencies in research findings. These include the presence of U-shaped or nonlinear or relationships between sustainability and financial performance [62]. Variations in model specifications and the utilization of different metrics to measure SSCP may contribute to these variations. Therefore, further research is required to gain theoretical and practical understanding regarding the mechanisms that could offer more insights into this relationship.

Past studies have indicated that there is a strong correlation between SSCP and an enterprise’s social performance: when a business engages in green efforts such as green marketing, green design and green purchasing, it boosts the enterprise’s social perception [56,63]. SP could be seen as an evaluation of important societal concern for the public [64]. According to Mardani et al. [2], a firm has a social responsibility to comply with all legal obligations and strive to enhance the well-being of society by minimizing damage to the environment. From this standpoint, customers are more likely to favor environmental responsible organizations [56]. Consumer concerns about the environment influence ethical business conduct and encourage the development of ecologically friendly products (such as green marketing) (Chowdhury and Quaddus [65], and increased financial success is obtained by fulfilling changing customer desires for services and goods that are eco-conscious [66].

The existing research has not yet explored the mediating role of social performance in the relationships between SSCP and economic performance. To our knowledge, this is first in the literature, and the logical progression of predictor influencing economic performance suggests that social performance plays a mediating role. An environmentally friendly industrial process is to create a systematic strategy for reducing waste and its impact on the environment, while also promoting a firm’s SP [2], and socially responsible operations can help businesses to attract a larger customer base, resulting in higher sales and overall market success [57]. In line with the above discussion, it is posited that:

H6: 

The SP of a firm mediates the relationship between SSCP and EP.

1.3.5. ENP as a Mediator

According to Yang and Wang [57], the main cause of environmental problems is our dependence on obsolete manufacturing processes. This reliance hinders our ability to effectively recycle and reuse products. To address these issues, many solutions are being developed with the aim of reducing environmental problems. From this perspective, green practices reduce hazardous emission and waste, helping businesses to obtain economic benefits. A firm with strict green standards may increase market share by offering environmentally conscious products made by utilizing green manufacturing procedures [47,67]. Increasing environmental performance can also lead to the establishment of new benchmarks in the industry while boosting the firm’s reputation and promoting its profit margins [68].

According to Bohlmann et al. [69], a firm’s performance is enhanced when it successfully meets the needs of its consumers. Tamayo-Torres et al. [66], financial success is closely linked to meeting the evolving consumer preferences for environmentally friendly products and services. Customer loyalty, which is essential for generating long-term profits, is affected by the level of satisfaction customers have with a firm’s treatment of the society where it operates [70]. The rise in environmental consciousness has led to an increase in customer willingness to pay premium prices for products that have a lower carbon footprint. From this standpoint, it is reasonable to infer that a firm’s ENP may mediate the relationship between its green practices and economic performance. Thus, it is posited that:

H7: 

The environmental performance of a firm mediates the relationship between SSCP and EP.

1.3.6. Industry 4.0 as a Moderator

As discussed earlier, the precise relationships between SSCP and the three sustainability dimensions (social, environmental and economic performance) are still under debate in the current literature. While the hypotheses in this study anticipate positive relationships by drawing from PBV, OIPT offers an alternative and complementary explanation to PBV and the inconsistent results. OIPT proposes that Industry 4.0 should improve manufacturing firms’ operational capabilities [27]. Environmental and operations management are information-intensive [7,27]. From an OIPT standpoint, digital technologies serve as the foundation of an enterprise’s information processing capabilities and promote decision-making procedures associated with environmental and operations management. Thus, it can be anticipated that digital technologies in the context of Industry 4.0 may contribute to further strengthening manufacturing organizations’ SSCP and sustainability through effective collection and processing of valuable information. The implications of Industry 4.0 on the economy, environment and society have attracted increased attention, as this emerging technological paradigm holds the potential to foster long-term sustainability [10]. However, the role of Industry 4.0 as an organizational condition in relation to the influence of SSCP on the three dimensions of sustainability simultaneously remains unexplored. In line with OIPT, it is proposed that Industry 4.0 technologies can serve as the driving force that can either weaken or further enhance the relationships in our integrated conceptual model (Figure 1), which will be elaborated upon below.

Industry 4.0 technologies, including cloud computing, IoT and big data, facilitate information flow between consumers and suppliers, support collaboration, and can lead to more effective environmental product design [42]. Li et al. [7] emphasized that through digital technologies in Industry 4.0, information related to energy efficiency about specific products and the level of wear can be communicated among supply chain members to assist in process innovation and product redesign while reducing a company’s environmental impacts. The aforementioned argument implies that the sharing of reliable and accurate information using Industry 4.0 technologies in the supply chain can help firms better their green practices (e.g., green design) and processes for environmental solutions.

Cloud computing enables firms to efficiently and intelligently analyze their internal process and make sustainable decisions regarding how to convert waste and by-products into higher-value products or utilize them as resources for other businesses (open- and closed-loop supply chains), hence not only promoting sustainability but also offering cost-effective solutions [71]. Therefore, it is reasonable to infer that Industry 4.0 can optimize the coordination and integration of internal processes, which can then be leveraged by manufacturing firms to mitigate their environmental footprint and promote resource efficiency, particularly in terms of economic performance.

An SSC, with its inherent social responsibility, stands as a fundamental pillar within the triple-bottom-line framework of a firm. The extent to which a firm prioritizes social responsibility plays a crucial role in determining its long-term success. However, Industry 4.0 provides valuable benefits for sustainability practices, particularly in areas such as green procurement, remanufacturing, reuse and recycling [17,72]. These benefits have been suggested to improve a firm’s social performance and consumer quality of life [2]. Based on the discussion above, it is anticipated that for manufacturing firms with high (low) Industry 4.0 use, the links between SSCP and sustainability dimensions (social, environmental and economic performance) are likely to be enhanced (weaker). Thus, we propose the following:

H8: 

Low Industry 4.0 usage weakens the positive effect of SSCP on social performance.

H9: 

High Industry 4.0 usage strengthens the positive effect of SSCP on environmental performance.

H10: 

High Industry 4.0 usage strengthens the positive effect of SSCP on economic performance.

2. Research Method

2.1. Research Context

For this study, we focused on the Turkish manufacturing industry for a number of reasons. Turkey’s 2023 Industry and Technology Strategy aims to grow the manufacturing sector’s share of the country’s GDP to 21% by 2023 from its current level of 18.83% over the past decade [73]. The government will allocate an annual investment ranging from 26 billion to 39 billion Turkish lira (i.e., 1–1.5 billion USD) towards the integration of Industry 4.0 solutions that pertain to the fourth industrial revolution and the digitalization of the manufacturing sector to enhance the manufacturing process [73]. The report further stated that Turkey stands to save $10 billion annually on manufacturing costs if it fully adopts the Industry 4.0 concept, with an anticipated 4–7% boost in productivity.

The sector is an interesting research context for our study because of the recent focus on sustainable manufacturing processes and initiatives, the integration of technologies into industrial operations and the nation as an important manufacturing and distribution hub, as well as its position as a bridge connecting Europe and Asia.

2.2. Research Design

The study’s research design is quantitative, specifically a cross-sectional research study. Cross-sectional studies can only observe data from a specific period of time and not predict results. In this quantitative study, we used a deductive approach to obtain data and used statistical tools for analysis. By employing a deductive method, various hypotheses were proposed to examine the effects and conditional effects among the constructs under observation. Using well-validated and established measurement items from prior research, primary data were collected by the researchers. Moreover, we obtained information of firms that prioritize sustainability and environmental protection, as well as firms that use digital technologies in their operations, and targeted these firms. We obtained responses from procurement managers, SC managers, plant managers, information system managers and operation managers of the surveyed manufacturing firms in Turkey. Responses were obtained via a self-administered procedure, and consultations were made in advance prior to data collection. The primary objective of data collection from these firms was to examine the influence of SSCP on sustainability and to examine the conditional role of Industry 4.0 technologies on the proposed relationships.

2.3. Data Collection and Sample

This study focuses on manufacturing firms in Turkey, specifically those in textile and apparel, food and beverages, medical and pharmaceutical, building materials, etc. (see

Table 2. Participants’ profile.

Demographic Information (n = 439)	Category	Frequency	Percentage
Gender	Female	77	17.54
	Male	362	82.46
Education	Bachelor’s	302	68.79
	Master’s	94	21.41
	PhD	4	0.91
	Others	39	8.89
Job position	Procurement manager	43	9.79
	SC manager	251	57.18
	Information system manager	49	11.17
	Plant manager	39	8.88
	Operation manager	57	12.98
Firm age	1–5	24	5.47
	6–10	154	35.08
	11–15	193	43.96
	Above 15	68	15.49
Firm size (number of employees)	Fewer than 25	26	5.92
	25–50	197	44.87
	51–75	151	34.40
	75–100	44	10.03
	Above 100	21	4.78
Business type	Textiles and apparel	58	13.22
	Food and beverages	135	30.75
	Wood and furniture	29	6.61
	Medical and pharmaceutical	26	5.92
	Plastics and rubber	8	1.82
	Chemical and petrochemicals	49	11.16
	Building materials	81	18.45
	Electrical and electronics	53	12.07

). Based on data provided by TOBB [74], there are more than 200,000 manufacturing firms operating in Turkey. We concentrated on firms listed in Turkey’s Trade Register Gazette. Since the authors were unable to survey all these enterprises, firms in Istanbul, Izmir, Konya, Bursa and Kocaali were selected for the current research. The selected cities are the top manufacturing cities in Turkey. Given the difficulties in accessing the entire population, this study adopted a non-probabilistic sampling method (i.e., purposive sampling) for data collection. To ensure participants’ anonymity, names and other identifying information were not included in the survey.

We approached chief operating officers, supply chain managers, production managers, etc. (see Table 2) for data collection. A total of 1834 questionnaire surveys were distributed via email and in-person visits. In sum, 439 valid responses were recovered, for a response rate of 23.94%. Given that the sample was drawn from Turkish manufacturing firms, the original survey design was translated into Turkish to remove any potential language barriers. Back-translation criteria suggested by Epstein et al. [75] and an expert committee were used.

Table 2 illustrates the demographic information of the respondents. In terms of gender, 362 (82.46%) were males and 77 (17.54%) females. Based on education, a majority of the respondents 400 (91.11%) had at least a bachelor’s degree, indicating the vast majority of the respondents in this study had sufficient education. In terms of job position, 251 (57.18%) were supply chain managers, 57 (12.98%) were operation managers, 49 (11.17%) were information system managers, 43 (9.79%) were procurement managers and 39 (8.88%) were plant managers. Firms aged 1–5 years numbered 24 (12.98%), 6–10 years 154 (35.08%), 11–15 years 193 (43.96%), and above 15 years 68 (15.49%). Firms with fewer than 25 employees numbered 26 (5.92%), 25–50 197 (44.87%), 51–75 151 (34.40%), 75–100 44 (10.03%), and above 100 21 (4.78%). Based on business type, textiles and apparel firms numbered 58 (13.22%), food and beverages 135 (30.75%), wood and furniture 29 (6.61%), medical and pharmaceutical 26 (5.92%), plastics and rubber 8 (1.82%), chemical and petrochemicals 49 (11.16%), building materials 81 (18.45%), and electrical and electronics 53 (12.07%).

2.4. Measures

Sustainable supply chain practices were measured using 4 items for green purchases, adopted from [67,76], 3 items for green design, adopted from [35,77,78] and 4 items for green marketing [36]. The participants were requested to rate the extent to which their firms had implemented the abovementioned SSCPs.

Social performance was measured using 3 items adopted from [39,79]. ENP was measured using 3 items adopted from [80].

Industry 4.0 (digital technologies) was measured using 4 items adopted from [81,82]. The respondents were asked to rate the extent to which their firms had applied digital technologies in their operations.

Economic performance was measured using 4 items adopted from [40,83]. The respondents were asked to rate the degree of improvement relating to economic performance of their firms since they had implemented sustainable supply chain practices.

2.5. Data Analyses

The data analyses for this study were conducted using SPSS version 25 and AMOS version 26. The measurement model was examined via confirmatory factor analysis (CFA) in AMOS 26. The PROCESS plug-in was employed to verify the mediation and moderated models in this research. The study employed model 4 and 8 to verify the mediation and moderated models, respectively.

2.6. Non-Response Bias

We conducted various preliminary tests to assess the quality of the data obtained. We employed wave analysis [84] to access non-response bias. The survey responses were divided into two groups based on early and late responses. An independent sample t-test was conducted on the survey items for sustainable supply chain practices and economic performance. No significant difference was observed in t values between the two groups, implying the absence of response bias in the data.

2.7. Common Method Bias (CMB)

To address the issue of CMB associated with cross-sectional studies, we adopted design and statistical procedures. The study was carefully designed to mitigate the possibilities of CMB. We adopted a mixed Likert scale to measure our independent, dependent, mediating and moderating constructs to rule out the possibility of survey participant-specific response patterns influencing our measurements [85]. Further, the complexity of this study’s integrated theoretical model concentrating on mediation and moderation (conditional effects) rendered it improbable for the participants to engage in theoretical speculation regarding the proposed relationships while answering the survey. We also followed [16] procedure for commonality bias by mixing and spreading the measurement items across the questionnaire. To encourage honest responses, we anonymized the participants [86]. Therefore, the data collected can be said to be reliable.

For statistical procedures, Harman’s first factor, common latent factor and marker variable tests was employed. It was discovered that the first factor explains 32.883% of the total variance (less than 50%) suggested by Podsakoff et al. [86], indicating that CMB is not a serious problem in our study. We also employed the latent factor test, where a single latent CMB factor was created and included in the CFA model. Poor model-fit indices were obtained on the single latent CMB factor (χ²/DF = 4.142, RMSEA = 0.130, TLI = 0.723, CFI = 0.714, RFI = 0.699, NFI = 0.706, IFI = 0.723, GFI = 0.604, AGFI = 0.594). The adopted conceptual model showed an excellent fit (χ²/DF = 1.888, RMSEA = 0.043, TLI = 0.973, CFI = 0.977 RFI = 0.945, NFI = 0.953, IFI = 0.977, GFI = 0.882, AGFI = 0.887). Lastly, following Lindel and Whitney’s [87] marker variable test, it was found that the unrelated marker variable had correlations of less than 0.020 with other constructs. Hence, it can be said that CMB is not a serious concern in our study.

2.8. Measurement Model

We checked the data collected for normal distribution. Lei and Lomax [88] recommended that for data to be normally distributed, skewness should be less than 2 and kurtosis less than 3. As illustrated in

Table 3. Reliability and validity.

Constructs		Indicators	Loadings	t-Value	SMC	Normal Distribution Skewness Kurtosis
Sustainable Supply Chain Practices
	Green Purchasing: α = 0.897, CR = 0.900, AVE = 0.696
		GP1	0.803	-	0.644	0.037	0.168
		GP2	0.709	16.953	0.503	−0.059	−0.263
		GP3	0.865	22.154	0.749	−0.078	−0.348
		GP4	0.944	24.425	0.891	−0.199	−0.893
	Green Design: α = 0.914, CR = 0.915, AVE = 0.782
		GD1	0.868		0.753	0.320	1.432
		GD2	0.888	25.704	0.789	−0.014	−0.064
		GD3	0.896	26.006	0.803	0.234	1.046
	Green Marketing: α = 0.941, CR = 0.942, AVE = 0.801
		GM1	0.940	-	0.884	0.369	1.654
		GM2	0.923	37.004	0.852	0.313	1.402
		GM3	0.889	32.953	0.790	0.334	1.493
		GM4	0.824	27.024	0.852	0.130	0.584
Social Performance	α = 0.859, CR = 0.847, AVE = 0.650
		SP1	0.728	-	0.530	−0.584	−2.616
		SP2	0.817	19.592	0.667	−0.420	−1.882
		SP3	0.868	18.316	0.753	−0.486	−2.178
Environmental Performance	α = 0.822, CR = 0.781, AVE = 0.544
		ENP1	0.817	-	0.668	−0.512	−2.294
		ENP2	0.660	15.802	0.436	−0.314	−1.404
		ENP3	0.728	17.942	0.530	−0.623	−2.790
Industry 4.0 (Digital Technologies)	α = 0.933, CR = 0.935, AVE = 0.784
		DT1	0.800	-	0.639	−0.323	−1.447
		DT2	0.925	24.459	0.855	−0.323	−1.448
		DT3	0.911	23.945	0.830	−0.471	−2.110
		DT4	0.901	23.576	0.812	−0.540	−2.417
Economic Performance	α = 0.885, CR = 0.889, AVE = 0.728
		EP1	0.908	-	0.825	−0.269	−1.205
		EP2	0.881	24.298	0.775	0.015	0.066
		EP3	0.763	20.218	0.582	−0.146	−0.654

Note: GP = green purchasing, GD = green design, GM = green marketing; DT = Industry 4.0 (digital technologies), SP = social performance, ENP = environmental performance, EP = environmental performance, α = Cronbach alpha, CR = composite reliability, AVE = average variance extracted.

, skewness values were from 0.015 to 0.623, while kurtosis values were from 0.064 to 2.616. Hence, the data used for the current study were normally distributed.

CFA was conducted to assess the measurement model of all the multiple-item latent constructs in this study. The reliability of individual items was evaluated by examining their loadings on the respective constructs and their internal consistency values. As illustrated in Table 3 and Figure 2, the results of the CFA revealed that all the items had high loadings on the corresponding constructs they were measuring. The loadings were all above (0.660 to 0.944) the recommended minimum threshold of 0.6. Further, all the values of composite reliability (CR) fell within the range of 0.781 to 0.942, profoundly above the cutoff value of 0.7 [89], implying that the measurement items in this study had sufficient reliability.

The assessment of divergent validity was performed through the calculation of shared variance among all possible pairs of constructs. This analysis aims to determine if the shared variance was less than the average variance extracted (AVE) for each construct [90]. As illustrated in Table 3, the AVE values for all the latent constructs in this study fell within the range of 0.544 to 0.801. These values exceed the minimum threshold of 0.5, demonstrating an acceptable level of convergent validity. In addition, the square root of all the AVEs were observed to higher than the inter-construct correlations, indicating discriminant validity was ensured in this study [91], as shown in

Table 4. Correlation and discriminant validity.

Constructs	M	SD	GP	GD	GM	SP	ENP	DT	EP	Firm Size	Firm Age	Edu
GP	3.783	0.741	(0.835)
GD	3.712	0.873	0.391 **	(0.844)
GM	3.752	0.885	0.393 **	0.502 **	(0.895)
SP	3.770	0.784	0.456 **	0.403 **	0.424 **	(0.806)
ENP	3.762	0.769	0.308 **	0.370 **	0.369 **	0.795 **	(0.744)
DT	3.105	0.621	0.337 **	0.281 **	0.417 **	0.382 **	0.390 **	(0.885)
EP	3.956	0.785	0.343 **	0.236 **	0.229 **	0.337 **	0.354 **	0.187 **	(0.853)
Firm age	3.124	0.731	0.099 **	0.134 **	0.067 **	0.202 **	0.114 **	0.084 **	0.223 **	-
Firm size	3.025	0.811	0.186 **	0.312 **	0.055 **	0.156 **	0.208 **	0.099 **	0.109 **	0.301 **	-
Edu	1.342	0.542	0.225 **	0.198 **	0.086 **	0.273 **	0.218 **	0.423 **	0.143 **	0.086 **	0.137 **	-

Note: M = mean, SD = standard deviation, ** = signifies significant correlate at 0.01.

.

Furthermore, as illustrated in

Table 5. Model fit results for CFA.

Metrics	χ2/DF	TLI	CFI	RFI	NFI	IFI	AGFI	GFI	RMSEA
1-factor model	4.142	0.723	0.714	0.699	0.706	0.723	0.597	0.604	0.130
2-factor model	3.865	0.747	0.752	0.729	0.718	0.746	0.613	0.629	0.116
3-factor model	3.125	0.849	0.861	0.828	0.822	0.849	0.711	0.724	0.100
5-factor model (research model)	1.888	0.973	0.977	0.945	0.953	0.977	0.882	0.887	0.043
7-factor model	2.89	0.891	0.900	0.869	0.894	0.891	0.745	0.769	0.091

, our adopted integrated theoretical model meets the recommended criteria for goodness of fit (GOF) in comparison to alternative models. It is worth mentioning that the adopted model is a second-order measurement. It has been argued that when theoretical foundations are strong, a second-order measure can be adopted in situations that offer a more parsimonious and interpretable model than the alternatives [92,93]. Table 5 demonstrates that the adopted five-factor model (SSCP, SP, ENP, Industry 4.0 and EP) provides a better fit than the alternative models. Further, as per model validation, we used GOF to examine if the adopted model provided a good fit for the data obtained. Absolute, relative and parsimonious fit indices are the three main types of indices employed in GOF [94]. The root-mean-square error of approximation (RMSEA), the adjusted goodness-of-fit index (AGFI), and the goodness-of-fit index are all absolute fit indices. Metrics of relative fit indices include the relative fit index (RFI), the incremental fit index (IFI), the normed fit index (NFI), the Tucker–Lewis Index (TLI), and the comparative fit index (CFI). The parsimony fit indices, which include the parsimonious goodness-of-fit index (PGFI), the parsimonious comparative fit index (PCFI), and the parsimonious normed fit index (PNFI). As presented in

Table 6. Fitness statistics results (research model).

Parameters	Limits	Results
Absolute fit
χ2/DF	>3	1.888
GFI	>0.8	0.887
AGFI	>0.8	0.882
RMSEA	<0.08	0.043
Incremental fit
TLI	>0.9	0.973
CFI	>0.9	0.977
NFI	>0.9	0.953
RFI	>0.9	0.945
IFI	>0.9	0.977
Parsimony fit
PNFI	>0.5	0.818
PGFI	>0.5	0.859
PCFI	>0.5	0.839

, the CFA results indicated our integrated theoretical model meets the specified criteria for assessing goodness of fit.

2.9. Direct and Mediation Analysis

We adopted model 4 of the PROCESS macro to examine the direct and mediation analyses. The results of the analyses of direct and mediation effects (parallel mediation) are illustrated in

Table 7. Direct and indirect effects.

Mediation Analysis: The Relationship between SSCP and EP Is Partially Mediated by Social Performance and Environmental Performance (PROCESS: Model 4 Bootstrap 95% CI
	B	S. E	t	ρ	LL	UP	R2
M1: mediator variable modelSustainable Supply chain Practices	Outcome: SP 0.530 0.049 10.806			0.000	0.434	0.627	0.196
M2: mediator variable modelSustainable Supply chain Practices	Outcome: ENP 0.639 0.045 14.153			0.000	0.550	0.727	0.295
M3: outcome variable model Economic Performance
Sustainable Supply chain Practices	0.262	0.060	4.383	0.000	0.144	0.379	0.167
SP	0.226	0.069	3.276	0.011	0.091	0.362
ENP	0.239	0.064	3.995	0.018	0.105	0.379
Indirect effects results via bootstrap
(Indirect effect of SSCP on EP through SP)	0.119	0.037			0.048	0.194
(Indirect effect of on EP through ENP)	0.208	0.034			0.053	0.229
Note: n = 439; M = model; bootstrap sample = 5000; LL = lower level; UP = Upper level = Upper level

. It was discovered that sustainable supply chain practices have a positive effect on social performance (β = 0.530, t = 10.806, p < 0.001) in model 1 of Table 7. Sustainable supply chain practices have a positive effect on ENP (β = 0.639, t = 14.153, p < 0.001) in model 2 of Table 7. Sustainable supply chain practices have a positive effect on economic performance (β = 0.262 t = 4.383, p < 0.001) in model 3 of Table 6. Social performance has a positive significant effect on economic performance (β = 0.226, t = 3.276, p < 0.05) in model 3 of Table 7. Environmental performance has a positive significant effect on economic performance (β = 0.239, t = 3.995, p < 0.05). Thus, H1, H2, H3, H4 and H5 were supported.

Furthermore, we employed the methods of both Baron and Kenny [95] and Preacher and Hayes [96] for mediation analysis. As illustrated in Table 7, the direct effect of sustainable supply chain practices was reduced after the inclusion of the mediators (social performance and environmental performance), revealing partial/complementary mediating effects. Subsequently, the results of the bias-corrected percentile via bootstrapping technique confirms the partial mediating effects: social performance β_{indirect effect} = 0.119, LL = 0.048, UL = 0.194, environmental performance β_{indirect effect} = 0.208, LL = 0.053, UL = 0.229). The confidence interval excludes both indirect effects. Hence, H6 and H7 were supported.

2.10. Moderation Model Test

We adopted model 8 of PROCESS to explore the moderating role of Industry 4.0 technologies on the relationship between sustainable supply chain practices and social performance (H8), sustainable supply chain practices and ENP performance (H9) and SSCP on EP (H10). In the moderation model test, we added firm age, firm size and education as covariates.

The results of the moderation model test are given in

Table 8. Testing moderation model.

Moderation Analysis: Industry 4.0 Moderated the Relationships between SSCP and Environmental Performance, and SSCP and EP (PROCESS Model = 8, CI = 95%). Bootstrap CI 95%
	B	SE	t	p	Upper	Lower	R2
M1: mediator variable model	Outcome: SP
Sustainable Supply chain Practices	0.495	0.053	8.227	0.000	0.299	0.448	0.209
Industry 4.0	0.136	0.078	3.045	0.027	0.117	0.328
Sustainable Supply chain Practices X Industry 4.0 (interaction)	0.045	0.030	0.097	0.851	−0.009	0.108
Co: Firm age	0.112	0.066	2.309	0.036	0.077	0.159
Co: Firm size	0.006	0.004	0.095	0.630	−0.104	0.073
Co: Education	0.038	0.036	0.082	0.711	−0.013	0.094
M2: mediator variable model Outcome: ENP
Sustainable Supply chain Practices	0.557	0.040	12.675	0.000	0.387	0.536	0.167
Industry 4.0	0.204	0.053	4.258	0.000	0.127	0.284
Sustainable Supply chain Practices X Industry 4.0 (interaction)	0.154	0.061	3.888	0.009	0.114	0.215
Co: Firm age	0.097	0.031	2.002	0.041	0.024	0.138
Co: Firm size	0.014	0.005	0.149	0.522	−0.031	0.069
Co: Education	0.009	0.006	0.092	0.472	−0.025	0.052
conditional direct effect of SSCP on ENP at different levels of Digital technologies
Industry 4.0 (−1SD)	0.126	0.068	2.563	0.037	0.065	0.164
Industry 4.0 (+1SD)	0.344	0.059	5.351	0.000	0.174	0.339
Model 3: dependent variable model	Dependent: Economic Performance
Sustainable Supply chain Practices	0.204	0.067	3.881	0.000	0.078	0.294	0.182
SP	0.189	0.070	2.842	0.019	0.113	0.362
ENP	0.257	0.049	5.366	0.000	0.163	0.352
Industry 4.0	0.196	0.072	3.011	0.007	0.128	0.294
Sustainable Supply chain Practices X Industry 4.0 (interaction)	0.192	0.053	3.002	0.034	0.057	0.316
Co: Firm age	0.086	0.040	1.926	0.039	0.022	0.095
Co: Firm size	0.004	0.008	0.071	0.623	−0.098	0.093
Co: Education	0.056	0.044	1.240	0.338	−0.066	0.108
The conditional direct effect of SSCP on EP at different levels of Digital technologies
Industry 4.0 (−1SD)	0.105	0.073	2.338	0.040	0.132	0.351
Industry 4.0 (+1SD)	0.449	0.056	7.223	0.007	0.376	0.594
Note: n = 439; M = model; bootstrap sample = 5000; LL = lower level; UP = Upper level = Upper level; Co = control variables

. A significant direct effect of SSCP on SP was observed in model 1 of Table 8 (β = 0.495, t = 8.227, p < 0.001) but the results indicated that this effect is not moderated by Industry 4.0 (β = 0.045, t = 0.030, p > 0.05). Hence, Industry 4.0 did not moderate the link between SSCP and SP, rejecting H8.

In model 2 of Table 8, it was discovered that the direct effect of SSCP on ENP was significant (β = 0.557, t = 12.675, p < 0.001) and the effect was moderated by Industry 4.0 (β = 0.495, t = 8.227, p < 0.001). To obtain further insights on the moderating effect of Industry 4.0, we examined the interaction via simple slope approach for both low and high levels of Industry 4.0 (see model 2 of Table 8, Industry 4.0 = M-1SD, and M + 1). The interaction was then plotted at M-1SD, and M + 1 of Industry 4.0 (see Figure 3). At low Industry 4.0 usage (β_{simple slope} = 0.126, t = 2.563, p < 0.05), the positive impact of SSCP on ENP diminishes for firms with low Industry 4.0 usage, providing support for H9. The relationship was further strengthened with high Industry 4.0 usage (β_{simple slope} = 0.344, t = 5.351, p < 0.001).

Furthermore, in model 3 of Table 8, it was found that the direct effect of SSCP on EP was significant (β = 0.204, t = 3.881, p < 0.001) and moderated by Industry 4.0 (β = 0.192, t = 3.002, p < 0.05). We then examined the strength of the relationship at low and high levels of Industry 4.0 using slope analysis to shed more light on the interaction (see model 3 of Table 8, Industry 4.0 = M-1SD, and M + 1). The interaction was then plotted at M-1 SD, and M + 1 of Industry 4.0 (see Figure 4). With high digital technology usage (β_{simple slope} = 0.449, t = 7.223, p < 0.01), the positive impact of SSCP on EP was further strengthened for firms with high Industry 4.0 usage, providing support for H10, while the relationship was weakened for firms with low Industry 4.0 usage (β_{simple slope} = 0.105, t = 2.338, p < 0.05).

3. Discussion

Based on the data collected from Turkish manufacturing firms, the current study examined the impact of SCCP on economic performance by drawing from PBV and OIPT theories as theoretical foundations. We also sought to offer insights regarding how SSCP impacts EP through social and environmental performance. We therefore set out to show how SSCP affects economic performance via the mediating roles of social and environmental performance. The moderating role of Industry 4.0 was also investigated.

The findings indicated that SSCP has a positive effect on economic performance. This outcome is consistent with the research of [10] and the conclusions of [47,49,52]. The alignment of this pattern of results could suggest that SSCP (such as green purchase, green design and green marketing) improves firm performance by decreasing waste, boosting operational efficiency and enhancing profitability [97]. Based on PBV [18], manufacturing firms that integrate sustainable practices into their operations will enhance their performance-related outcomes, such as economic performance, as confirmed by the current research. SSCP has positive effects on social and environmental performance. These results align with the suggestions of prior studies [53,55] that green practices would improve a firm’s SP and ENP. The observation here is that through SSCP, firms can achieve waste reduction, efficient energy use, remanufacturing and recycling, as well as meeting the social and environmental expectations of customers. Further, the strong positive impact of SSCP on social performance could imply that these practices help firms in meeting both legal and ethical obligations, ultimately leading to financial advantages.

Social and environmental performance were found to be predictors of economic performance. These results aligned with the conclusions of recent research that reported that adopting socially and environmentally responsible practices have the potential to increase customer attraction [48,58,98], thus improving EP.

Social and environmental performance were revealed to play a complementary (partial) mediating roles on the relationship between SSCP and EP. These results align with the conclusions of [2,47,67]. These results could indicate that manufacturing firms’ social and environmental performance further aid and help to establish a balance between their green practices and economic outcomes, ultimately leading to financial advantages. Social and environmental performance drives firms to assume responsibility for environmental, societal, customers’ and other stakeholders’ requirements, which in turn generate financial benefits for firms.

Furthermore, it was discovered that the higher the use of Industry 4.0, the stronger the impact of SSCP on environmental performance. Echoing the organizational information processing theory [19], the emergence of Industry 4.0 is driving firms towards greater efficiency by leveraging automation and interconnecting capacities. Manufacturing processes such as cleaner production require advanced technologies that can promote operational efficiency and sustainability. The link between SSCP and environmental performance was stronger for firms with high usage of Industry 4.0 technologies. This indicates that organizations with high adoption of Industry 4.0 technologies will experience tremendous improvements in their SSCP with the aim of promoting their environmental performance. This might be because SSCP will further contribute positively towards firms’ environmental efforts when the integration of Industry 4.0 technologies into the manufacturing processes is high.

Finally, the link between SSCP and EP was stronger for firms with high adoption of Industry 4.0 than those with low Industry 4.0 adoption. This particular result is crucial for manufacturing firms, because environmental information regarding a firm’s operation can be obtained where necessary from the authority in the operating country. Moreover, in some cases, environmental information is openly shared transparently with customers and other stakeholders. Where a firm’s SSCP prioritizes environmental concerns of customers and other stakeholders, they would be happy to identify with the firm while also attracting new customers that value environmental protection, thereby increasing the profitability and establishing a competitive advantage for the firm.

3.1. Theoretical Contributions

This research adds to our knowledge of SSCP and the three sustainability dimensions in several crucial areas. While the existing body of knowledge has placed serious attention on SSCP, it is apparent that the social dimension of this phenomenon has received considerably less attention [10,99]. The current study highlights the importance of considering the social and environmental performance of a firm, which has been largely overlooked in the existing literature, which primarily focuses on economic performance. First, it is worth mentioning that by drawing from PBV and OIPT, this is the first study to use this integrated theoretical model in this way. Second, there has been relatively less research on the influence of SSCP on SP compared to that on its influence on operational performance [100], due to the fact that social performance has been studied mostly as an isolated concept in previous studies, leading to conceptual ambiguity regarding its potential effect on firm performance [101]. The discovery of a positive effect of SSCP on social and environmental performance from the perspective of the Turkish manufacturing industry expands the existing body of knowledge. Therefore, the findings of the study not only extend the existing body of knowledge regarding SSCP in relation to the triple-bottom-line concept [1] but also offer new empirical evidence that support PBV and OIPT theories [18,19].

Third, the current study explicitly offers empirical evidence of how social and environmental performance affect economic performance, a significant addition to the existing body of knowledge. Fourth, a recent study reported that SSCP significantly affects economic performance [10]. However, not much is known regarding the mechanisms through which this relationship occurs. This study proposed and offered empirical evidence that social and environmental performance are mechanisms through which SSCP affects economic performance, closing the research gap identified by Khan et al. [10]. Hence, social and environmental performance indirectly improve a firm’s outcomes in this manner.

Fifth, prior related studies have examined Industry 4.0 as a predictor of sustainability [7,10]. Given that Industry 4.0 is a very nascent area of study, the available body of evidence pertaining to the effects of contingencies is quite limited. In this study, by combining PBV and OIPT, we examined Industry 4.0 as an organizational condition. We found a positive moderating role of Industry 4.0 on the relationship between SSCP and ENP, and SSCP on EP, a novel contribution that is not presently found in the existing literature. Thus, we offer insight that Industry 4.0 will help in SSCP and sustainability integration in the manufacturing industry. Additionally, our study helps to understand under what condition Industry 4.0 further influences SSCP (i.e., green practices) and promote sustainability.

3.2. Managerial Contributions

Our study has provided valuable recommendations that managers can effectively implement. It is important for manufacturing firms to be more active in building sustainable supply chains. Based on the results of our research, we suggest that decision-makers adopt a comprehensive approach to implementing an integrated SSC strategy. Through sustainable supply chain practices, firms should combine various green practices, as demonstrated in our study, to obtain competitive advantage. Green practices should not solely concentrate on establishing partnerships with suppliers to facilitate green purchasing but also include green marketing aimed at customers and green design for operations in the entire supply chain.

It is recommended that managers prioritize establishing a comprehensive cooperation network among every functional unit, manufacturers, suppliers and customers by establishing a closely integrated supply chain that partners and plan together, rather than symbolizing the standard of environmental management.

The outcomes of our research provide managers with new insights regarding how SSCP can be used to improve firm performance. Despite the direct influence of SSCP on economic performance, our study indicated that SSCP influences economic benefits indirectly through social and environmental performance. It is therefore recommended that managers refrain from making assumptions regarding only the direct benefits of SSCP on financial advantages. When developing a firm’s rationale for implementing SSCP, it is crucial for managers to prioritize the identification of key SSCPs that can promote social and environmental performance, because maximizing the use of resources may be difficult if the primary focus is solely on financial benefits.

The empirical findings revealed the effect of SSCP on ENP and SSCP on EP is stronger based on the level of Industry 4.0 adoption. Industry 4.0 technologies such as cloud computing, BDA and IoT play a crucial role in processing and analyzing the volume of data produced during environmental product design and intelligent manufacturing. By combining sustainable supply chain practices and digital capabilities, organizations can effectively facilitate genuine digital transformation while also reaping environmental and economic advantages. Therefore, it is recommended that managers wanting to promote their environmental efforts intensify the use of Industry 4.0 technologies to develop effective environmental efforts, which in turn results in financial performance.

3.3. Limitations and Future Research Calls

The study has limitations that present potential areas for future research. The research sample consisted of Turkish firms, which could constrain the generalizability of the research findings to developing countries with different environmental approaches and technological capabilities. Future studies should focus on increasing the sample size and expanding the scope of the survey for more comprehensive research. For instance, conducting a comprehensive survey of related firms in both developing and developed nations for additional verification will be a great avenue for future studies.

In terms of sustainable supply chain practices, our research utilizes the three components of green purchase, green design, and green marketing to measure SSCP. Future studies should add and combine other factors that may have impact on firm performance, such as green branding and sustainable supply chain partner selection. Future research that employs different theoretical perspectives can also offer deeper understanding of the links between SSCP and sustainability.

Lastly, future studies should examine other mediators and moderators that may either complement or act as a substitute in the link between SSCP and sustainability. It would be fascinating if future research were to examine the role of leadership of supply chains or logistic managers and top management commitment. These organizational factors may influence implementing SSCP on sustainability.