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Article

Green Manufacturing for a Green Environment from Manufacturing Sector in Guangdong Province: Mediating Role of Sustainable Operations and Operational Transparency

by
Zulkaif Ahmed Saqib
1,2,
Gang Xu
1,* and
Qin Luo
1,2,*
1
College of Applied Sciences, Shenzhen University, Shenzhen 518060, China
2
College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen 518118, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2024, 14(22), 10637; https://doi.org/10.3390/app142210637
Submission received: 24 September 2024 / Revised: 12 November 2024 / Accepted: 14 November 2024 / Published: 18 November 2024
(This article belongs to the Section Environmental Sciences)
Browse Figures
Versions Notes

Abstract

:
This work investigates the manufacturing operations of focal firms to manage the enhancement of environmental sustainability (EnS). To achieve this, indirect and direct effects of operational transparency (OPT) and sustainable operations (SUP) between environmental business practices (EBPr) and EnS are proposed. By leveraging the resource-based view theory, this study seeks to clarify how integrating transparency and sustainable operations can enhance a firm’s ability to manage environmental challenges effectively. Aligning environmental business practices with sustainable operations and transparency concepts appears to be an appropriate choice for environmental sustainability. A well-designed questionnaire was formed and used to collect the observations from 1214 focal firms. FsQCA and SEM approaches are employed to analyze one of the research questions of operations management: How do OPT and SUP mediate the effects of EBPr on the environmental sustainability of a firm? The final results clarify that the indirect effects of OPT and SUP significantly completely exist and are positive. The findings describe that firms with operational transparency and sustainability perform well in resolving operational and sustainable issues.

1. Introduction

Green manufacturing processes are vital for focal firms as they enable significant benefits across multiple fronts [1,2]. The manufacturing sector, recognized for contributing considerably to environmental issues, such as pollution and resource depletion, is under increased observation and pressure from governments, regulatory bodies, and the public. Firms can enhance their environmental responsibility and comply with stringent regulations, mitigating operational risks and improving their reputation, by managing environmental issues through energy efficiency, waste reduction, and resource conservation in business operations [3,4]. To manage environmental impact, the adoption of sustainable practices is crucial for a firm that requires resource efficiency [5], operational efficiency [6,7], pollution prevention [8], adoption of renewable energy [9], and a sustainable supply chain [10]. The pressing need for environmental business practices (EBPr) has emerged as a cornerstone in modern corporate strategy, driven by increasing environmental concerns and regulatory pressures [11,12]. EBPr refer to the policies, strategies, and actions that a company adopts to reduce its negative impact on the environment and promote sustainability [11]. EBPr are designed to align business operations with environmental standards and goals.
Today, scholars are investigating how digitalization has affected business operations and provides various benefits that support boosting a firm’s operations [13,14]. Sanders et al. [15] argued that emerging technologies support firms in unveiling a myriad of new opportunities and bringing a new kind of business level by leveraging digital assets. Even recently, the acknowledgment of the transformative potential of digital technologies and applications is largely rooted in sustainability studies [15,16]. Digital technologies (DTs), such as information-based technologies (IoTs), big data analytics (BDA), and blockchain technology, are allowed in industrial firms to enhance transparency in operations [17,18]. Operational transparency (OPT) refers to the degree to which a company openly shares information about its internal processes, decision-making, and operations with stakeholders to achieve a specific goal [13,14,19]. In underpinning operations administration, advanced applications and technologies have improved internal and external operational integration [19,20]. Junaid et al. [14] confirmed that firms require information-based resources, such as BDA and IoT technologies, to manage their processes for high transparency in business operations. Business research lacks a theoretical and practical understanding of how digitalization influences the strategic choices and actions of firms. Moreover, digitalization helps business operations by enhancing efficiency, making the best use of available resources, and assisting company tactics [11]. One prominent aspect of DTs is their role in accelerating interest in operational performance by adopting blockchain technology [21]. In addition, Longo et al. [16] explained that blockchain technology enables the tracking of supply chains, allowing for firms to demonstrate their fidelity to environmental sustainability (EnS). Stability in technological resources for environmental protection, social progress, and economic growth is crucial, which can boost sustainable strategy [22]. For stability in EnS, sustainable operations (SUP) are essential for businesses aiming to achieve long-term growth while contributing positively [23]. A practice enables firms to operate in a way that preserves the environment for future generations and positions them as leaders in responsible business practices. Aligning operations with sustainability standards helps ensure compliance with environmental regulations, reducing the risk of penalties and fines. However, this study revealed that EnS in a firm needs an absent investigation on the mediating roles of OPT and SUP. An unrecognized attempt to assist in raising awareness of the operational efficiency of environmental issues is not well-researched. Based on the above discussion, we formulate the following research questions:
RQ1: What obstacles to managing environmental sustainability are relevant in the case of business operations?
RQ2: Whether operational transparency and sustainable operations elucidate the mediating effect between environmental business practices and environmental sustainability?
Indeed, dependence on business operations is based on advanced DTs, such as BDA and IoTs, which can improve the capacity of processes to enhance internal and external resources [16,19]. While firms face numerous obstacles in managing environmental sustainability, these challenges can be mitigated through strategic planning, innovative technologies, adaptive policies, and a cultural shift towards sustainable management. The first obstacle for firms is a lack of transparency, where sharing information can be an obstacle to managing environmental sustainability. Second, inaccurate information could be an obstacle for a firm because incomplete information can lead to misunderstandings and poor decision-making. Last, transparency could be an obstacle because of data sensitivity, which is contained in confidential information to maintain a competitive edge.
Transparency is essential for businesses, particularly in business-to-business (B2B) relationships, where firms with high transparency are more successful in implementing and communicating environmental initiatives [24,25]. Principally, when considering both the positive and negative aspects of DT integration, the existing body of resource-based view (RBV) theory falls short of capturing the intricate dynamics in the digitalization of firms. These concerns require new solutions promoting transparency and improved information exchange across industries to boost performance in businesses [26]. Liu et al. [27] argued that transparency and information exchange give a clearer picture of what is happening within the supply chain. For industrial processes, transparency is an essential strategy that improves coordination and collaboration among supply chain partners [28]. This research provides new insights into RBV theory by introducing links among sustainable operations, operational transparency, environmental business practices, and environmental sustainability, where SUP deal with the firm’s carving behavior for operations improvement and OPT deals with sharing information about their operations improvement. Therefore, healthy theoretical foundations would be imperative to unpack the potential of digital technology for enhancing environmental sustainability, as firms would demand fundamental adaptations for environmental business practices within their operations and their underpinning business models, structures, and culture to align with the sustainability-oriented mindset [29,30].
Firstly, a construct-based model was designed including proposed hypotheses among OPT, EBPr, SUP, and environmental sustainability (see Figure 1). In the literature review portion, ten hypotheses are proposed. The methodology for data collection and analysis is presented in the next portion. In the final part, results discussions, research implications, research conclusions, and research limitations are explained.

2. Literature Review

Theoretical development: Ten hypotheses for this investigation rely on the resource-based view (RBV) theory. The RBV theory is a strategic management framework that focuses on the internal resources and capabilities of a firm as sources of competitive advantage [31,32]. RBV theory is most prevalent in industrial research, which helps to understand how resources influence the collaborative work of companies that enhance novel competitive advantages [33]. Technological resources are essential for influencing firm performance across various industries [31]. In the current digital period, a critical theoretical lens of the eye is RBV usage for business effectiveness [34]. RBV helps achieve the highest level of organizational operations to enhance competitive advantages by using capabilities for knowledge, information, and data management [35]. By following the above discussion, RBV, as an ability of a firm, supports all capabilities and resources for victory and competitive advantage [36,37]. Thus, managing resources (tangible and intangible) and assets by firms will improve environmental initiatives. The existing body of resource-based view (RBV) theory falls short of capturing the intricate dynamics in the digitalization of firms. This research provides new insights into RBV theory by introducing links among sustainable operations, operational transparency, environmental business practices, and environmental sustainability. Figure 1 shows the hypothetical relations for this work, which depend on RBV logic on achieving sustainable and operational outcomes of firms. This work defines and introduces the OPT concept as intangible resources.
Hypothesis development: Ten hypotheses for this investigation rely on the resource-based view (RBV) theory. A company’s environmental, operational, economic, and social performance are influenced by business practices and considerations at every supply chain phase throughout the life cycle of a product, beginning with product design and continuing through the distribution procedure, the product’s supply chain, and its end-of-life management [30]. Investigations on firms adopting sustainable development into their operations have identified numerous advantages [12,14]. In addition to cost reductions, various other benefits have been achieved, including enhanced productivity, improved financial performance, boosted employee morale, increased organizational commitment, greater efficiency, less environmental impact, and improved public image, among others [38,39,40]. The effective execution of innovative methods and procedures by leaders is crucial for organizational change, enhanced performance, and growth, considering that the approaches used in business operations can significantly minimize the consumption of energy, materials, and other resources [41], considerably reducing the impact on the environment while enhancing the value of the good or service [42]. Since operating expenses will be lower due to improved environmental performance [43], the company’s profits will rise [44]. These activities include adopting sustainable practices in the supply chain, building particular environmental competencies inside the company, and prioritizing strategic orientations towards eco-innovation and eco-reputation [45]. Environmental practices help companies perform better and gain an edge over their competitors [14]. Similarly, the idea of environmental practices is evaluated from several angles, which are considered multidimensional [22]. Sustainable manufacturing, distribution, reverse logistics, and sustainable procurement are sub-dimensions of environmental business practices that are essential for advancing sustainability issues in the industrial sector [5]. These practices are accessible to improve the company’s sustainability performance [11], including eco-design, green information systems, operational methods for internal management, and customer support. First, implementing environmental business practices, such as reducing waste, conserving energy, and using eco-friendly materials, directly contributes to minimizing the environmental impact of a company’s operations. EBPr could help in reducing pollution, lowering carbon footprints, and preserving natural resources, thus enhancing environmental sustainability. Second, environmental business practices lead to more efficient use of resources and a reduction in waste and emissions. So, EBPr promote operational efficiencies and sustainable use of inputs, resulting in operations that are less harmful to the environment and more resilient over time. Finally, by adopting environmental business practices, firms often need to track, report, and disclose their environmental impacts and efforts. This necessity leads to greater operational transparency, as the company provides more information to stakeholders about its environmental performance and sustainability initiative. Based on this discussion, the following hypotheses are proposed.
H1. 
Implementing environmental business practices increases operational transparency in manufacturing firms.
H2. 
Implementing environmental business practices improves the environmental sustainability in a manufacturing firm.
H3. 
Implementing environmental business practices improves the sustainable operations within manufacturing firms.
In operational literature, OPT has become most imperative and modern in operational research, which can achieve sustainable and supply chain performance [46,47]. OPT is the level of access to relevant information by supply chain partners [3]. OPT concentrates on information access, which is helpful for the tracking procedure of raw material, inventory, and flow of goods within the supply chain in an appropriate manner [1]. OPT can immediately respond to business plan changes, such as changes according to inventory, market, and demand [48]. The information recorded under the OPT notion is enormously essential to improve control of the supply chain network, which is helpful for required performance [48,49]. In addition, recorded information under OPT conception provides a way to reduce hazards, which leads to improvement in time and quality within the supply chain network [4,50]. OPT guarantees that high-quality information is available, including market information that helps reduce operational costs by minimizing inventory costs and enhancing the supply chain’s agility [51]. Sharing knowledge is essential for the decision-making process since it offers a complete picture of the supply chain with precise and comprehensive detail, which helps to make perfect decisions from existing options and substitutes [52]. Based on internal and external resources, OPT demands combining efforts to collect information about upstream and downstream activities in the supply chain to obtain operational transparency [46,53]. In a competitive environment, OPT assists an enterprise in managing supply chain flaws and modifications [54]. Transparency allows for tracking resources and completed items from their point of origin to the end of the sale [55]. External operational transparency deals with information about goods and products from the external resources of a firm, and internal transparency refers to information about the firm’s operations [52]. In the literature, the direct consequences of information sharing are investigated as a component of operational transparency [56]. Somapa et al. [46] argued that operational transparency positively influences supply chain performance in terms of quality and information about products. Thus, operational transparency assists in improving firms’ capabilities because, when a firm is transparent about its operations, it builds trust with stakeholders and encourages accountability. Transparency often leads to continuous improvement as stakeholders (customers, regulators, investors) push for better environmental performance, thus supporting environmental sustainability. Operational transparency involves openly sharing information about business processes and environmental impacts, which can drive sustainable practices as stakeholders demand and the firm itself strives for improvements in sustainability, making operations more efficient and eco-friendlier. For the OPT concept, the following hypothesis can be expected after a detailed discussion.
H4. 
Operational transparency positively influences the sustainable operations within manufacturing firms.
H5. 
Operational transparency positively influences the environmental sustainability in manufacturing firms.
H6. 
Operational transparency positively mediates the connection between environmental business practices and sustainable operations.
H7. 
Operational transparency mediates the relationship between environmental business practices and environmental sustainability in manufacturing firms.
Operational performance encompasses the degree of efficiency and effectiveness in internal operations, which directly affects productivity, cost-effectiveness, and the delivery of commodities [19]. Improving a firm’s competitiveness requires increasing operational performance, which involves optimizing procedures, maximizing the utilization of resources, and improving decision-making [31,57]. Operational abilities, when paired with strategic initiatives and investments in technology, are crucial in determining the accomplishment of objectives [32]. For transparency in the supply chain as well as firm performance, in the literature, several concepts and relationships are investigated based on business operations. For example, information sharing enhances operational performance, particularly when high-quality information is exchanged [58]. Difrancesco et al. [59] found a connection between sustainable supply chain management strategies, such as information exchange, and improved operational and sustainability performance. The synergistic impact of combining RFID technology and information sharing on JIT, TQM capabilities, and subsequent operational performance was sighted by [60]. In detail, sustainable operations, characterized by the efficient use of resources, minimal waste production, and reduced emissions, inherently support the broader goal of environmental sustainability. Further, transparency in operations encourages the adoption of sustainable practices, as stakeholders can observe and assess the firm’s environmental performance. So, sustainable operations contribute to achieving environmental sustainability goals. Three hypotheses are proposed as follows:
H8. 
Sustainable operations have a positive effect on the environmental sustainability of manufacturing firms.
H9. 
Sustainable operations positively mediate the connection between environmental business practices and operational transparency.
H10. 
Sustainable operations positively mediate the connection between environmental sustainability and operational transparency.

3. Research Design and Methodology

The current research was investigated by using smartPLS 3.0 statistical software. Through smartPLS 3.0, confirmatory factor analysis (CFA), exploratory factor analysis (EFA), path analyses, covariance structure models, first-order technique, second-order technique, countless regression-based analysis, and correlation structure models are all handled [61,62]. Further, smartPLS 3.0 helps scholars of management and social sciences to appraise hypothetical models. This study employs the structural equation modeling (SEM) method for proposed theoretical relations. The SEM method is convenient for the linear examination of relationships among manifest constructs and latent variables [63]. In addition, Byrne [64] explained that the SEM approach is a flexible and robust technique for building a computable statistical structural model. Researchers explained that SEM could estimate data’s dependability, trustworthiness, and validity [62]. In addition, this technique allows for scholars to examine complex structural and statistical models based on multi-levels, for example, mediating, moderating, and other complex links among constructs.
Questionnaire Development: The current research developed a survey questionnaire, which was designed from literary works. A team of experts was arranged to finalize the questionnaire, including six professors and six professionals belonging to relevant areas of this work. Ideas from the experts helped to improve the questionnaire by removing contradicting constructs. Further, we assessed the experts’ recommendations and finalized the questionnaire after modifications. All of the items on the scale were taken from the literature. For example, the EBPr construct has five adapted items [65,66]; the SUP construct has five adjusted items [67]; the OPT concept has six adapted scale items [68]; and seven items were adapted for environmental sustainability [65]. Before data collection, we assured the participant’s that the information collected during data collection would be confidential and asked them to return the filled questionnaire. The final questionnaire had two parts to record the responses from the participants. The first part of the last questionnaire records demographical information, such as gender, working experience, job specification, firm age, firm registration, firm size, and number of employees. The second portion of the final questionnaire records feedback on 23 scale items. A 5-point Likert scale (strongly agree = 5, agree = 4, neither agree nor disagree = 3, disagree = 2, and strongly disagree = 1) was employed for scale items.
Data collection: In the current work, the elected population for sampling belonged to focal firms located in 21 cities in the Guangdong province of China (see Figure 2). Tian Yan Cha (China’s national database) was analyzed to start a data collection procedure to generate a list of companies. For the unit of analysis, registered and active companies were elected from this national database. According to Figure 2, Shenzhen city has the highest number of important companies, with 294 responses. Before data collection, 35 online answers were gathered in order to pre-test analysis for moving forward. The results for the pre-test described that the reliability and factor loadings of the items were good and met the 0.70 threshold [69].
Data for the final analysis were based on 1214 samples. During data collection, we contacted companies through email and WeChat and visited them in person for a high response rate. The final responses show that the level of awareness for environmental sustainability is quite higher in males. Further, the 20–24 respondents are more aware than other age groups due to their higher interest in green environments. Similarly, the respondents who worked in <50 employee firms (small firms) are more aware because of the workload and focus of operational activities. The 2–4 year-old firms are more aware than other firms due to the competitive environment for sustainability administration. Finally, environmental sustainability awareness is also high for sole-ownership firms (see Table 1).
In addition, the demographical information of the collected data is revealed in Figure 3. The report for gender explains that 45.3% were male and 54.7% were female out of 1214 samples. The statistics specified that, out of the final sample size, 52.39% of participants had sole ownership, 29.74% of respondents had partnerships-based investments in a company, 15.4% of respondents were employees/owners of a limited company, and 2.47% of respondents were working as a group sharing investment with a firm. A total of 30.89% of participants worked in <2 year-old firm, 35.50% of participants were engaged with a 2–4 year-old firm, 16.39% of respondents belonged to a 4 to 6 year-old company, 10.63% of respondents were working at a 6–8 year-old company, and 6.59% respondents worked in a >6 year-old firm. The firm size is signified by the number of employees in Figure 3. Of the 1214 participants, 67.03% were working at companies of <50 employees, 18.28% participants were working at companies of 51–100 employees, 12.29% responders were working at companies of 101–150 employees, 2.1% respondents were working at companies of 151–200 employees, and 0.3% respondents were working for firms with 200 plus employees.
FsQCA analysis: The FsQCA method was used to find causal conditions of independence, which theoretically lead to the final outcomes. All causal conditions of independence were among OPT, EBPr, SUP, and environmental sustainability, firm size, job experience, and job specification, where environmental sustainability was considered as an explanatory construct, EBPr was considered as a core explanatory construct, and OPT and SUP were considered as intermediary constructs.
  • O P T i t = a 0 + a 1 E B P r i t + a 1 c o n t r o l s i t + f i r m   a g e + f i r m   s i z e + f i r m   r e g i s t e r a t i o n + έ (H1)
  • S U P i t = b 0 + b 1 E B P r i t + b 1 c o n t r o l s i t + f i r m   a g e + f i r m   s i z e + f i r m   r e g i s t e r a t i o n + έ (H2)
  • E n S i t = c 0 + c 1 E B P r i t + c 1 c o n t r o l s i t + f i r m   a g e + f i r m   s i z e + f i r m   r e g i s t e r a t i o n + έ (H3)
  • S U P i t = d 0 + d 1 O P T i t + d 1 c o n t r o l s i t + f i r m   a g e + f i r m   s i z e + f i r m   r e g i s t e r a t i o n + έ (H4)
  • E n S i t = e 0 + e 1 O P T i t + e 5 c o n t r o l s i t + f i r m   a g e + f i r m   s i z e + f i r m   r e g i s t e r a t i o n + έ (H5)
  • S U P i t = q 0 + q 1 E B P r i t + q 1 O P T i t + q 1 c o n t r o l s i t + f i r m   a g e + f i r m   s i z e + f i r m   r e g i s t e r a t i o n + έ (H6 inclusion of H1)
  • e E n S i t = f 0 + f 1 E B P r i t + f 1 O P T i t + f 1 c o n t r o l s i t + f i r m   a g e + f i r m   s i z e + f i r m   r e g i s t e r a t i o n + έ (H7; inclusion of H1)
  • E n S i t = e 0 + e 1 S U P i t + e 1 c o n t r o l s i t + f i r m   a g e + f i r m   s i z e + f i r m   r e g i s t e r a t i o n + έ (H8)
  • O P T i t = q 0 + q 1 E B P r i t + q 1 S U P i t + q 1 c o n t r o l s i t + f i r m   a g e + f i r m   s i z e + f i r m   r e g i s t e r a t i o n + έ (H9 inclusion of H2)
  • e E n S i t = f 0 + f 1 E B P r i t + f 1 S U P i t + f 1 c o n t r o l s i t + f i r m   a g e + f i r m   s i z e + f i r m   r e g i s t e r a t i o n + έ (H10; inclusion of H1)
where equations (H1, H2, H3, H4, H5, and H8) represent the empirical mechanism for classifying the direct connection among EnS, OPT, SUP, and EBPr. Equations (H6, H7) and (H9, H10) represent the empirical mechanism for OPT and SUP, respectively. Further, in the equations, the castoff of the subscript i and t is classifying firm and year.
During FsQCA, calibrated values for each construct must be calculated with values ranging from 0 to 1 for transformed fuzzy sets. Ragin [70] explained that nearly 1 calibrated value represents full set membership, 0 calibrated value represents no set membership, and 0.5 calibrated value denotes no set membership. However, in our research, the full membership calibrated value is 5, the non-membership calibrated value is 2, and the crossover calibrated value is 3. Further, calibrated constructs were calculated through mean values of corresponding items of constructs.
In the FsQCA method, the configuration of the causal condition was analyzed through consistency metrics, which include empirical relevance of the subset and statistical significance [71]. The first step for the FsQCA was the necessity analysis of our data, which verified the condition (consistency value should be >0.74, and coverage should be >0.27) of whether OPT and EBPr are present/absent in all cases where SUP and environmental sustainability are present/absent [72]. In our research, two different models for SUP and environmental sustainability were executed during FsQCA. We produced a truth table from the analysis of sufficiency through the FsQCA algorithm (see Table 2). The results for the EnS model show that the consistency among EBPr, OPT, and EnS is 0.891 with 0.721 coverage, which means EnS cannot be achieved without OPT and EBPr. The consistency results for SUP are showing 0.843 with 0.690 coverage, and the analysis explained that SUP depends on OPT and EBPr. The combination of EBPr and OPT for a firm’s performance outcome is contributed in all solutions.
Factor Analysis: Before the SEM technique, factor analysis was executed through smartPLS 3.0 software to examine the model assessment. To verify the data’s reliability and validity, the exploratory factor analysis (EFA) method was employed as factor analysis. For model fitness verification, the squared Euclidean distance (d_ULS), the geodesic distance (d_G), RMS Theta, standardized root mean square residual (SRMR), normed fit index (NFI), and chi-square (Chi2) values were examined. The threshold value for NFI should be closer to one, and for SRMR, it is <0.08 for the good fit index of the model [62]. First, descriptive statistics were explained with mean, standard deviation, skewness, and kurtosis values for each item (see Figure 4). Constant variance and normality for all construct items were assessed from the skewness and kurtosis values. Further, the kurtosis and skewness values for each scale item were calculated at their most significant categorical level, which was evaluated and found to be within the limits of data reliability. The threshold values for skewness and kurtosis are <2 and <7 individually [73]. Moreover, variance inflation factor (VIF) values were considered to examine the multicollinearity aberrant. Multicollinearity among factors influences the route coefficients, and the threshold of VIF value is <5.0 [73]. VIF values are less than 5.0, indicating no multicollinearity in the final data (see Table 2). Thus, the results show that the instrument is helpful for further study, and EFA was used to determine each variable’s dimensionality.
Data reliability was examined by scale composite reliability (SCR) and average variance extracted (AVE) values. The threshold values for SCR and AVE are above 0.70 and 0.50, respectively [73]; the calculated values for AVE and SCR were acceptable (see Table 3) and passed the reliability test. Data validity was examined by discriminant validity (DV) and convergent validity (CV). The heterotrait–monotrait ratio of correlations (HTMT) and Fornell–Larcker criterion were used for DV confirmation (see Table 4). DV was verified from the square root of the AVE values. The basic rule for DV is that the values of the correlations between the variables should be less than the square root of AVE. The results showed that the correlation values between variables are lower than the square roots of the AVEs. The results showed that DV does not exist among multiple cross-loadings for each scale item. Next, CV was assessed using values for factor loading of each scale item. The calculated values for factor loading were more than 0.50, which shows suitability. The analysis included those scale items with factor loading >0.50. Further, the results for SCR values were significant in favor of loadings sufficient to confirm CV.
SEM analysis: In the SEM method, the significance levels of p = >0.05, p = >0.01, and p = >0.000 are used for acceptance/rejection decisions, which scholars recommend [62]. The conceptual framework includes four constructs with seven hypothetical relationships. Six direct and four indirect relationships among EnS, EBPr, OPT, and SUP constructs were proposed in the SEM model. In smartPLS software, seven latent variables and 23 indicators for item scales were developed to analyze the collected data. After entering the raw data, numerous phases were performed during the analysis, such as data pre-processing, defining all constructs, and producing links among constructs to acquire the results.
The measurement of model fit is assessed by ensuing the SRMR, chi-square (χ2), NFI, d_ULS, and d_G values. The standard value of SRMR is <0.08, which indicates the structural model’s good fit and suitability. The estimated and statured value of SRMR is 0.031, which meets the standards. The threshold for the NFI value is higher than 0.80, and the founded value for NFI is 0.962 (see Table 5). The value of χ2 is 782.744 for the saturated model. The overall model fit results indicate that the model’s fitness is good and acceptable.
For structural hypotheses testing, path coefficient (β) standard errors, t-values, and p-values are stated in Figure 5 and Table 6, where the path coefficient specifies the change in outcome variable due to the predictor variable for every 1-unit, t-value is the coefficient divided by its standard error, and p-value is a significance level. The statistical results for H1 show the positive effect of EBPr on OPT, with 0.149 coefficient (β) value, 4.341 t-value, and p≤ 0.05, confirming the proposed relationship. The calculated coefficient (β), t-value, and p-value are 0.072, 2.28, and <0.000, which claimed a significant impact of EBPr on SUP for hypothesis H2, which is supported. EBPr had a favorable impact on EnS and supported the fictitious H3 hypothesis, with calculated coefficient (β), t-value, and p-value of 0.257, 9.931, and <0.000, respectively. The results show that the direct and mediating effects of OPT are significant and supported. The direct effects of OPT on SUP and EnS are significantly positive and validated with β(0.233), t-value(7.368), and p≤ 0.000 for hypothesis H4; and β(0.259), t-value(9.509), and p≤ 0.000 for hypothesis H5. The mediating effect of OPT among EBPr and SUP (H6) is positive with β = 0.035, t-value 4.123 and p≤ 0.000, which significantly confirms the proposed hypothesis. The mediating effect of OPT among EBPr and EnS (H7) is positive with β = 0.039, t-value = 4.154, and p≤ 0.000, which significantly confirmed the proposed hypothesis. The direct effect of SUP for H8 was positive and significantly confirmed. The statistical result for H8 is (β = 0.231, t-value = 8.730, and p≤ 0.000). Therefore, the mediating effect of SUP among EBPr and EnS (H9) is positive with β = 0.054, t-value = 5.435, and p≤ 0.000, which significantly confirmed the proposed hypothesis. Last, the mediating effect of SUP among EBPr and EnS (H10) is positive with β = 0.017, t-value = 2.237, and p≤ 0.05, which significantly confirmed the proposed hypothesis. According to the results, the mediating role of SUP is higher than the mediating role of OPT, which is possible due to the enhanced quality of environmental features linked with all business operations. However, all hypotheses were significantly positive and confirmed with the proposed hypothesis.

4. Discussion

The current research expands the body of research on sustainable supply chain management (SSCM) by integrating it with operational research. Two key findings emerge when investigating the effects of operational transparency (OPT) and sustainable operations (SUP) on environmental sustainability as an operational driver. First, the results address RQ1, revealing that several obstacles to managing environmental sustainability in business operations include factors such as resource management, advanced technology, and governmental regulations and standards, which hinder the implementation of sustainable business practices. To overcome these operational challenges, a holistic approach is needed—one that integrates sustainability principles across various business functions and emphasizes the pivotal role of leadership in driving sustainable strategies. The findings offer a distinctive approach to environment management research, as most existing studies examining these connections are qualitative, with large-scale quantitative analyses still being quite rare [74]. Comparatively, the contributions are novel, and the outcomes of the current study are supported by literary works [74,75,76]. For example, the effects of EBPr towards OPT, EnS, and SUP were positive and significant. This means that EBPr adoptions have achieved the purpose of adoption toward the EnS at focal firms. In other words, high transparency in business operations, adoption of EBPr in business operations, and sustainability in operations motivate focal firms to highlight environmental issues. This finding supports the literature that asserts that business practices benefit ecological concerns [75,77,78].
Second, this study aims to determine whether OPT and SUP mediate the relationship between environmental business practices (EBPr) and environmental sustainability (EnS) concerning RQ2. Our findings confirm that achieving environmental sustainability requires high levels of transparency in environmental business practices. These results show the importance of operational transparency to manage business practices for accuracy in business procedures. The current findings are supported by previous literature [78,79]. Academic researchers argue that close communication between business partners can enhance the overall performance of the supply chain [79]. For example, if key information from suppliers or customers is shared with relevant business partners (e.g., raw material suppliers, wholesalers, manufacturers, or third-party participants), partners can update their operations accordingly, improving the efficiency and effectiveness of the firm. Next, this study provides positive and significant mediating effects of sustainable operations and new grounds for the resource-based view theory. The finding reveals that organizational strategy for environmental sustainability relies on sustainable operations, which are supported by previous literature [23,80,81]. Thus, the results also confirm the role of operational transparency and sustainable operations in enhancing environmental sustainability. In other words, the influence of the OPT and SUP constructs strengthens the relationship between environmental business practices and environmental sustainability.
The results of this study contribute to the growing body of operational research on small firms by highlighting the mediating role of sustainable operations and operational transparency as key factors in adopting environmental business practices in the manufacturing sector. Data were collected from the manufacturing industry in Guangdong Province, China, which serves as a significant industrial hub. Therefore, we believe these findings could also benefit other regions of China. While this research underscores the importance of transparency and sustainability for business operations, we also suggest that these findings may apply to other manufacturing sectors across China. Furthermore, this study emphasizes the importance of environmental sustainability in improving innovation efficiency. Although environmental sustainability can be assessed with clear benchmarks and quantitative criteria, evaluating social sustainability in manufacturing remains challenging due to its more ambiguous goals and standards. The positive effect of OPT reveals that firms inevitably face sustainable and operational risks when engaging in tight collaborations, such as sharing internal information with supply chain partners. Through operational transparency, firms can enhance their ability to implement EBPr, respond to environmental concerns, and contribute to a more environmentally responsible business environment.
The findings show that supply chain members prioritize the potential benefits of tight collaboration over the possible risks of sharing internal information. Additionally, the results reveal that OPT increases the effectiveness of EBPr, leading to improved sustainable and operational performance. This indicates that updated information system technology allows for business partners in a supply chain to optimize sustainability and operational outcomes. Therefore, transparency in sharing information about environmental business practices is essential in the manufacturing sector, where businesses interact with numerous suppliers, manufacturers, and customers.
Confirming OPT’s impact on SUP highlights the critical role of transparency in improving operational and sustainable performance among business partners. The resource-based view (RBV) theory guides inter-organizational relationships as a source of competitive advantage. The results confirm that information as a strategic resource is valuable to the exchange of information itself. The resource-based view (RBV) attributes strongly predict OPT, except for joint management structures in upstream business partnerships. To strengthen their sustainability and operational resilience, firms should establish solid alliances. Strategically, OPT should be regarded as an asset gained through partnerships that achieve operational excellence and sustainable performance. This survey shows that EBPr enhance business processes by promoting extensive information sharing. Additionally, through OPT, EBPr significantly impact SUP, suggesting that business processes become increasingly complex and compelling over time. However, the long-term implications for sustainability require a more thorough evaluation. Ultimately, OPT supports adopting and integrating EBPr into business operations, resulting in improved sustainability and operational efficiency.

5. Theoretical and Managerial Implications

From a theoretical perspective, the current research provides new contributions to the literature by focusing on a firm’s transparency function, including operational and sustainability performance. For example, this study recognizes antecedents of OPT based on the RBV logic of inter-organizational links in terms of sharing information. RBV logic focuses on the resources and capabilities of a firm. For theoretical contributions, firstly, the mediating impact of OPT was analyzed among the EBPr and SUP of a firm. Secondly, the mediating influence of OPT among EBPr and EnS was examined. Third, the direct theoretical impact of EBPr on OPT was examined. Fourth, this research provides a clear picture and understanding of transparency. Lastly, the current work also provides a theoretical implication after testing a theoretical model in the context of a company. The manufacturing industry is knowledge-intensive and heavily based on novel information about updated products. Thus, understanding information sharing for operational and sustainable issues will provide new information on how focal firms can adopt environmental business practices that would create the capability to enhance firm performance. This study will also increase the importance of updated technology for firms to resolve issues, which will help adopt best practices in operations.
For supply chain managers and practitioners, this work offers some implications. The results confirmed that OPT is beneficial for EBPr to improve the sustainability and operational issues of a firm. Thus, firstly, companies should use OPT as a strategic resource in a positive way to maximize a firm’s performance. Companies can apply OPT from a trustable, formal, safe joint structure to resolve conflicts. Secondly, the current work explored the impact of environmental business practices, which projected that corporate administrations outline their ideas for OPT resources. With the help of a partner’s cooperative orientation, management implication stems from a study assessing environmental business practices’ effect on a firm’s performance outcomes. Lastly, problems related to sustainability and operations can be resolved by exchanging knowledge about business practices. Managers should absorb the OPT resource that can advance the impact on a firm’s overall performance, which may generate hurdles for managers.

6. Conclusions

The analysis concludes that, by integrating these environmental business practices into their operations, companies can effectively reduce their ecological footprint, conserve natural resources, protect ecosystems, and contribute positively to environmental sustainability goals on a local and global scale. This general approach not only benefits the environment but also supports long-term business resilience and profitability in an increasingly environmentally conscious marketplace. Overall, these relationships emphasize the interconnected nature of environmental practices, operational transparency, and sustainable operations in driving environmental sustainability. Understanding and leveraging these relationships can help businesses enhance their sustainability performance, build stakeholder trust, and achieve long-term operational and environmental benefits. The manufacturing sector needs an impulse behavior to adopt all those policies, which helps to improve the firm’s performance. With helping behavior, small companies should gather knowledge and information from business partners to strengthen their performance.
This investigation does provide some limitations for scholars. First, the findings are based on high context dependence and are influenced by the focal industry (specific industry characteristics), which is limited. Second, this study highlights the need for more thoughtful evaluation of environmental concerns, but it does not deeply explore the other types of sustainability. Third, this work chose the manufacturing sector for data collection. In the future, other sectors (e.g., logistics, agriculture, and education) of China should be studied. Fourth, while this study suggests that EBPr impact business processes and SUP through OPT, the interplay of these variables can be more complex than represented. Other influencing factors, such as market conditions and technological advancements, might be captured in the future. Finally, data were collected from the Guangdong province, China. This work could be extended to explore the scope of research to other regions for extensive results.

Author Contributions

Conceptualization, Z.A.S.; methodology, Z.A.S.; software, Z.A.S.; validation, Z.A.S., G.X. and Q.L.; formal analysis, Z.A.S.; investigation, Z.A.S.; resources, Z.A.S.; data curation, Z.A.S.; writing—original draft preparation, Z.A.S.; writing—review and editing, Z.A.S., G.X. and Q.L.; visualization, Z.A.S.; supervision, G.X. and Q.L.; project administration, Z.A.S., G.X. and Q.L.; funding acquisition, G.X. and Q.L. Z.A.S. is principal investigator (PI). All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by Department of Education of Guangdong Province (No. 2022KCXTD027), Guangdong University Engineering Technology Research Center for Precision Components of Intelligent Terminal of Transportation Tools (No.2021GCZX002), Shenzhen UAV Test Public Service Platform and Low-altitude Economic Integration and Innovation Research Center (No. 29853MKCJ202300205).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to sensitive information.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Construct-based model.
Figure 1. Construct-based model.
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Figure 2. Location of companies in Guangdong province (N = 1214).
Figure 2. Location of companies in Guangdong province (N = 1214).
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Figure 3. Stats of demographics (N = 1214).
Figure 3. Stats of demographics (N = 1214).
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Figure 4. Descriptive stats: SUP = sustainable operations, EnS = environmental sustainability, OPT = operational transparency, EBPr = environmental business practices.
Figure 4. Descriptive stats: SUP = sustainable operations, EnS = environmental sustainability, OPT = operational transparency, EBPr = environmental business practices.
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Figure 5. Results for SEM. Where, “***” represents p-value≤ 0.000, and “*” represents p-value≤ 0.05.
Figure 5. Results for SEM. Where, “***” represents p-value≤ 0.000, and “*” represents p-value≤ 0.05.
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Table 1. Environmental sustainability awareness.
Table 1. Environmental sustainability awareness.
Environmental Sustainability Awareness Has Started Improving the Implementation of Environmental Business Practices.YesNO
Count%Count%
GenderMale59250.95%2549.02%
Female57049.05%2650.98%
Age<20 years33729.00%1019.61%
20–24 years42336.40%47.84%
25–28 years20917.99%917.65%
29–32 years1028.78%1121.57%
33–36 years615.25%1019.61%
37–40 years201.72%611.76%
>40 years110.95%11.96%
Firm Size<50 Employees78167.21%3160.78%
51–100 Employees21118.16%1223.53%
101–150 Employees14112.13%815.69%
151–200 Employees262.24%00.00%
>200 Employees40.34%00.00%
Firm Age<2 years old35830.81%1733.33%
2–4 years old42136.23%1019.61%
4–6 years old19016.35%917.65%
6–8 years old12110.41%815.69%
>8 years old736.28%713.73%
Firm RegistrationSole Ownership60752.24%2956.86%
Partnership35130.21%1019.61%
A limited company 17615.15%815.69%
A group-based firm262.24%47.84%
Table 2. FsQCA outcomes.
Table 2. FsQCA outcomes.
Model: EnS = f (Firm Age, Firm Registration, Firm Size, EBPr, OPT); Intermediate Solution
EnSEBPrOPTFirm sizeFirm RegistrationFirm AgeConsistencyRaw CoverageUnique Coverage
1⦸⦸⦸🗹⦸0.7700.5690.014
2⦸⦸⦸⦸🗹0.6560.8110.026
3⦸⦸🗹⦸⦸0.7410.7140.038
4🗹🗹⦸⦸⦸0.8910.7260.013
Model: SUP = f (firm age, firm registration, firm size, EBPr, OPT); Intermediate solution
SUPEBPrOPTFirm sizeFirm RegistrationFirm AgeConsistencyRaw coverageUnique coverage
1⦸⦸⦸🗹⦸0.7320.5430.014
2⦸⦸⦸⦸🗹0.6510.8080.040
3⦸⦸🗹⦸⦸0.7190.6960.045
4🗹🗹⦸⦸⦸0.8430.6900.013
Note: SUP = sustainable operations, EnS = environmental sustainability, OPT = operational transparency, EBPr = environmental business practices. ⦸ = not configure, 🗹 = configure
Table 3. Factor analysis.
Table 3. Factor analysis.
Item ScalesEnSEBPrSUPOPTVIFSCRAVEAlpharho_A
EBPr10.2860.8810.1070.1322.8580.9390.7540.9180.924
EBPr20.2530.8480.0820.1092.462
EBPr30.2660.8570.0740.1212.550
EBPr40.2760.8620.0790.1172.595
EBPr50.3040.8920.1160.1622.956
EnS10.7000.1990.2530.2291.5750.9030.5710.8740.876
EnS20.7940.2430.2490.2951.969
EnS30.7420.2230.2320.2751.707
EnS40.7540.2680.2370.2561.741
EnS50.7620.2440.2390.2611.803
EnS60.7590.2590.2220.2801.771
EnS70.7730.2530.2680.2711.833
SUP10.3000.1210.8940.2152.2180.9520.7980.9370.940
SUP20.2520.0850.8850.1823.209
SUP30.2900.0820.8880.2312.065
SUP40.2900.0850.8860.2193.044
SUP50.2980.1010.9120.2363.737
OPT10.3300.1300.2170.8973.6170.9630.8140.9540.955
OPT20.3010.1280.2070.9082.129
OPT30.3420.1450.2350.9121.090
OPT40.3110.1240.2120.9001.749
OPT50.3080.1340.2170.8962.622
OPT60.3200.1450.2280.9011.801
Note: Bold values are loadings. SUP = sustainable operations, EnS = environmental sustainability, OPT = operational transparency, EBPr = environmental business practices, VIF = variance inflation factor.
Table 4. Discriminant validity.
Table 4. Discriminant validity.
Fornell–Larcker Criterion
ConstructsEnSEBPrSUPOPT
EnS0.755
EBPr0.320.868
SUP0.3210.1070.893
OPT0.3540.1490.2430.902
Heterotrait-Monotrait ratio of correlations (HTMT) criterion
ConstructsEnSEBPrSUPOPT
EnS1
EBPr0.3551
SUP0.3540.1131
OPT0.3860.1570.2561
Note: SUP = sustainable operations, EnS = environmental sustainability, OPT = operational transparency, EBPr = environmental business practices.
Table 5. Model fitness of the structural model.
Table 5. Model fitness of the structural model.
CatalogsSaturated Model
Standardized root-mean-square residual (SRMR)0.031
Unweighted least squares discrepancy (d_ULS)0.272
Geodesic discrepancy (d_G)0.110
Chi-square (χ2)782.744
Normed fit index (NFI)0.962
Table 6. SEM analysis findings.
Table 6. SEM analysis findings.
HypothesisCoefficients (β)Standard Deviation (STDEV)T Statistics (|O/STDEV|)p-Values
EBPr―>EnS0.2570.0269.9310.000
EBPr―>SUP0.0720.0322.280.023
EBPr―>OPT0.1490.0334.5530.000
SUP―>EnS0.2310.0268.730.000
OPT―>EnS0.2590.0279.5090.000
OPT―>SUP0.2330.0327.3680.000
EBPr―>SUP―>EnS0.0170.0072.2370.026
OPT―>SUP―>EnS0.0540.015.4350.000
EBPr―>OPT―>EnS0.0390.0094.1540.000
EBPr―>OPT―>SUP0.0350.0084.1230.000
Note: SUP = sustainable operations, EnS = environmental sustainability, OPT = operational transparency, EBPr = environmental business practices.
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Saqib, Z.A.; Xu, G.; Luo, Q. Green Manufacturing for a Green Environment from Manufacturing Sector in Guangdong Province: Mediating Role of Sustainable Operations and Operational Transparency. Appl. Sci. 2024, 14, 10637. https://doi.org/10.3390/app142210637

AMA Style

Saqib ZA, Xu G, Luo Q. Green Manufacturing for a Green Environment from Manufacturing Sector in Guangdong Province: Mediating Role of Sustainable Operations and Operational Transparency. Applied Sciences. 2024; 14(22):10637. https://doi.org/10.3390/app142210637

Chicago/Turabian Style

Saqib, Zulkaif Ahmed, Gang Xu, and Qin Luo. 2024. "Green Manufacturing for a Green Environment from Manufacturing Sector in Guangdong Province: Mediating Role of Sustainable Operations and Operational Transparency" Applied Sciences 14, no. 22: 10637. https://doi.org/10.3390/app142210637

APA Style

Saqib, Z. A., Xu, G., & Luo, Q. (2024). Green Manufacturing for a Green Environment from Manufacturing Sector in Guangdong Province: Mediating Role of Sustainable Operations and Operational Transparency. Applied Sciences, 14(22), 10637. https://doi.org/10.3390/app142210637

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