Green Intellectual Capital and Green Supply Chain Performance: Does Big Data Analytics Capabilities matter?

In light of global environmental concerns growing, environmental awareness within firms has become more important than before, and many scholars and researchers have argued the importance of environmental management in promoting sustainable organizational performance, especially in the context of supply chains. Thus, the current study aimed at identifying the impact of the components of green intellectual capital (green human capital, green structural capital, green relational capital) on green supply chain performance in the manufacturing sector in Jordan, as well as identifying the moderating role of big data analytics capabilities. To achieve this aim, we developed a conceptual model of Structural Equation Modelling-Partial Least squares and tested through the Smart-PLS software on a sample of 438 respondents. Empirical results showed that each of the components of green intellectual capital and big data analytics explains 71.1% of the variance in green supply chain performance and that all components of green intellectual capital have a statistically significant impact on green supply chain performance. The results also revealed that the relationship between green relational capital and green supply chain performance is moderated through big data analytics capabilities. Finally, this study made a theoretical and managerial implications to the supply chain literature and industry.


Introduction
Because of the changes taking place in the local and global market and business environment, the increase in competitiveness, globalization and various and numerous disturbances, which have presented Frare and Beuren (2021) asserted that the environmental aspect is, therefore, no longer a luxury for rms, but a strategic and competitive necessity to create competitive advantages.
According to the resource-based view (RBV) theory, rms and organizations can use the results of their various resources and combine these resources with different activities to generate added value for their products and services, thereby creating sustainable competitive advantages (Nagano, 2020). With the importance of RBV in understanding the ability of companies to create a competitive advantage through optimal exploitation of resources, several scholars (see Hart, 1995;Hart & Dowell, 2011) posit that rms' interest in the surrounding external environment maximizes their use of natural resources and reduces waste, thus leading to their competitive advantage (Hamdoun, 2020) and the natural resource-based view (NRBV).
Supply chain sustainability is one of the basic aspects of creating added value for customers (Avilés-González et al., 2017). The supply chain is a group or network of interconnected relationships that aim to increase pro tability and create added value for customers. Firms' interest in the ow of products and services from production to customers leads to customer satisfaction and creates a competitive advantage (Ivanov et al., 2017). With the environmental turmoil that the world is witnessing in this period, rms' interest in promoting environmental initiatives in the supply chain will improve their optimal use of resources, increase e ciency and reduce waste (Permatasari et al., 2022); help them achieve economic, social and environmental sustainability (Sony, 2019); and help them achieve environmental responsibility, which has become a strategic orientation for international rms to improve their brand imageparticularly in light of the organizational and moral pressures on these rms to become more oriented towards environmental responsibility (Liu et al., 2021; Park et al., 2022).
Phuah and Fernando (2015) de ned green supply chain management as 'management that focuses on the traditional aspects of the supply chain with greater attention to environmental issues and issues' (p. Xx). Firms' orientation of their organizational capabilities towards the environment enhances the growth of the green supply chain (Ramakrishna, 2020). The success of environmental initiatives in the supply chain can be measured by several indicators, including providing environment-friendly products and services, adding new methods of packaging and designing processes oriented towards the environment or reducing the waste of energy and natural resources in the supply chain ( Shou et al., 2018b). Firms' exploitation of their human resource knowledge and skills and the optimal use of their intellectual capital enhances their ability to innovate, leading to the creation of new ways to improve the supply chain, thus improving performance for the chain and adding value to customers.
From an environmental management perspective, green intellectual capital is described as the sum of existing knowledge and skills used within the rm in organizational and environment-oriented processes and activities and which give the rm a competitive advantage (Malik et al., 2020; Yusliza et al., 2020). The knowledge-based view theory posits that green intellectual capital contributes to increasing the exchange of knowledge and experiences among individuals within a rm, as well as increasing and sharing knowledge at the organizational level, particularly the environmental knowledge and skills required to achieve sustainable performance (Yong et  . Moreover, big data analytics can be used to improve decision-making and solve various problems that may arise in supply chain channels  as well as improve supply chain performance because the supply chain is highly dependent on information (Xiang et al., 2021). Thus, big data analytics can provide rms with greater control over the supply chain and reduce risks and disruptions from external causes. According to Kache and Seuring (2017), rms' use of big data analytics in supply chain management improves the performance level of the chain, leading to a competitive advantage.
With the importance of big data analytics capabilities in improving supply chain performance, numerous studies have addressed the huge potential in big data's use in environmental initiatives and trends (El-Kassar & Singh, 2019). Big data analytics contributes to providing statistical and mathematical models such as predictive models that can be used to discover new patterns that contribute to increasing general and environmental innovations (Borah et al., 2021; Chen et al., 2006) as well as improving organizational activities and increasing the e ciency of processes, leading to reducing waste in the supply chain ( RQ2. Do big data analytics capabilities have a moderator role in the relationship between green intellectual capital (green human capital, green structural capital and green relational capital) and green supply chain performance in the manufacturing sector in Jordan?
The remainder of this manuscript is organized as follows: Section 2 reviews the theoretical framework and hypotheses development; Section 3 presents the methodology; Section 4 presents data analysis and results; and Section 5 presents the discussion and conclusion.

. Green Human Capital and Green Supply Chain Performance
The green supply chain is a modern trend to reduce environmental risks and waste in the supply chain (Jabbour et al., 2016;Sarkis, 2012). It is a concept that focuses on integrating environmental dimensions and aspects as well as the traditional dimensions and aspects of the supply chain (Ramakrishna, 2020).
The green supply chain focuses on assessing the environmental effects of products at all stages of production up to the nal customer (Lam et al., 2015). Firms' tendency to use environmental management in supply chain activities leads to stopping waste and increasing e ciency along the supply chain (Islam et (2021), have emphasized the importance of integration within these chains, such as green integration with customers, which is described as environmental cooperation and the sharing of information related to environmental aspects with key customers to improve green practices within the supply chain (Shah & Soomro, 2021), and integration with suppliers, which focuses on building long-term cooperative partnerships between the rm and key suppliers, particularly strategic coordination in concerning environmental initiatives and activities and taking joint decisions related to environmental aspects (Zhang et al., 2020). Green internal integration, which is the main pillar for the success of the green supply chain, focuses on coordinating the organizational efforts undertaken by the senior management and sharing these efforts with all organizational units and departments that focus on planning and implementing environmental programs in the supply chain (Song et  From the previous discussion, the following hypothesis can be made: H1: Green human capital positively affects green supply chain performance.

Green Structural Capital and Green Supply Chain Performance
Structural capital refers to the organizational capabilities that rms own that transform the ideas and innovation of staff into tangible and realistic assets that can be referred to at any time and used and exploited within these rms ( The rms' orientation towards social responsibility practices and attention to environmental issues plays a key role in improving the level of green structural capital (Maaz et al., 2021). The focus of senior management on green orientation makes these rms more interested in transferring the experiences and knowledge of employees, which represent green human capital, into routine internal knowledge (Londoño & Espinosa, 2021; Secundo et al., 2020) used for green programs and initiatives.
Several studies have con rmed that green structural capital is positively associated with enhancing sustainable business performance (Khanlarov et al., 2020) as well as creating competitive advantages (Arie et al., 2019). In the context of supply chains, green structural capital can enhance green supply chain performance by supporting knowledge exchange among employees, rms and suppliers to achieve success in environmental initiatives (Ullah et al., 2022). Green structural capital can further help rms exploit their technological and knowledge capabilities to achieve environmentally oriented organizational goals and thus achieve high performance in the supply chain concerning preserving the environment From the previous discussion, the following hypothesis can be assumed: H2: Green structural capital positively affects green supply chain performance.

Green Relational Capital and Green Supply Chain Performance
Through the network of relationships that they own, rms can create added value and a competitive In the environmental context and in environmental preservation, green relational capital refers to the rm's relationships with the main stakeholders on environmental aspects and environmental management that contribute to the rm's competitive advantage (Atiku, 2019). Green relational capital provides information about the rm's social responsibility and its progress in implementing its environmental plans (Chen et al., 2013), building trust between the rm and its main stakeholders (Holgado, 2019). It is also possible for the rm to bene t from green relational capital through increasing organizational and individual learning capabilities (Benevene et al., 2021), which increases the knowledge within the rm and thus leads to new green innovations (Rehman et al., 2021).
Several studies in the literature have con rmed that there is a positive relationship between green relational capital and green supply chain performance. Wu  Firms that focus on building strategic relationships based on environmental considerations can increase cooperation in quality management, leading to more e cient products and increasing supply chain performance by reducing operational costs in the supply chain (Wu et al., 2020). According to the social capital theory, one of the basic requirements for supply chain success is to establish long-term cooperative relationships among the rm, suppliers and all cooperating parties (Wu et al., 2012). Thus, green relational capital enhances cooperation in the green supply chain, leading to increased performance (Yu & Huo, 2019). Claro et al. (2006) emphasized that cooperation in the green supply chain improves response to environmental and operational risks as well as knowledge sharing among all these parties.
From the previous discussion, the following hypothesis can be assumed: H3: Green relational capital positively affects green supply chain performance.

The Moderating Role of Big Data Analytics Capabilities
Big data analytics refers to modern technical tools that provide the ability to manage and process data of a large and diverse size (Lalmi & Adala, 2021). Big data analytics focuses on processing big data, which can provide new knowledge and can be used and exploited to reach innovations or nd solutions to various problems (Chen et  Several empirical studies have discussed the role of big data analytics within the supply chain. The use of big data analytics enhances the improvement of operations and manufacturing e ciency to achieve better performance within the supply chain (Li et al., 2015), thereby creating new value for customers (Rejeb et al., 2022). Big data analytics also reduces the product development and manufacturing cycle, leading to reduced waste in the manufacturing processes (Ogbuke et al., 2022). Mikalef et al. (2019) expressed the importance of big data analytics because of its many new technologies and techniques designed to create economic value from large data by providing new ways to access knowledge, discover and seize opportunities.
According to Iftikhar and Khan (2022) and Sanders (2014), supply chain management makes use of big data analytics techniques to better understand what is going on and to thus facilitate decision-making in the supply chain as well as to provide better predictions about market trends and customer preferences leading to reducing costs. Furthermore, big data analytics provides real-time supply chain management, which provides the rm with quick ways to make decisions and signi cantly lower risk ratios (Janssen et al., 2017; Tiwari et al., 2018). Because the relationship between big data analytics and supply chain management is clear, several scholars have called this relationship the concept of supply chain analytics (Wang et al., 2016), which focuses on using the organizational capabilities that rms own to exploit big data to improve the e ciency of activities (Tiwari et al., 2018).
Although the literature concerning supply chain management has proven the importance of big data analytics capabilities in improving supply chain performance , empirical research that has examined the positive relationship between big data analytics and green supply chain management is still relatively scarce (Dubey et al., 2020). Although the emergence of Fourth Industrial Revolution technologies has provided an opportunity to exploit these technologies to improve supply chain performance, there is an important opportunity to use these technologies to improve sustainable performance in the supply chain (Belhadi et al., 2021a). Big data analytics can be used to improve levels of sustainability through the design of environment-friendly products (Singh et al., 2018)  Big data analytics can boost green supply chain performance by exploiting data from different external sources that create new opportunities for collaboration with suppliers and key stakeholders in the environment-related decision-making process (Benzidia et al., 2021). In addition, big data analytics can enhance internal cooperation within the supply chain by sharing information extracted from this data in real-time, which improves the e ciency of internal processes and activities (Vecchiato, 2012). Big data analytics can also provide predictive modeling and simulation techniques that improve the company's processing capabilities and thus improve the internal green supply chain performance by moving towards improving green internal processes, such as logistics, warehousing and supplies within the rm (Wang et al., 2016). Papadopoulos et al. (2017) emphasized that big data analytics is useful with regard to reducing the negative effects of environmental disasters, which may signi cantly affect public health, thereby alleviating disruptions in the green supply chain. Integrating the big data analytics capabilities into the green supply chain reduces the critical consequences of operational or environmental risks that may harm the surrounding environment (Pandey et al., 2021), thus improving environmental and sustainable performance (Belhadi et al., 2021b).
Based on the previous discussion, the following hypotheses can be assumed: H4: Big data analytics moderates the relationship between green human capital and green supply chain performance.
H5: Big data analytics moderates the relationship between green structural capital and green supply chain performance.
H6: Big data analytics moderates the relationship between green relational capital and green supply chain performance. Figure 1 shows the hypothetical study model that examines the causal relationships between exogenous and endogenous constructs.

Methodology
This study examined the causal relationships between green intellectual capital (green human capital, green structural capital and relational green capital) on green supply chain performance in the manufacturing sector in Jordan and the moderating role of big data analytics capabilities. To achieve the study's objectives, the quantitative-deductive causal method was used, which focuses on testing hypotheses and causal relationships (Wilson, 2014). This approach allows for testing the relationships between different constructs statistically so that it provides an empirical understanding of the relationships between the constructs (Sekaran & Bougie, 2016). To collect the study data, cross-sectional data was used by distributing the questionnaire to the study sample, and, therefore, the data was collected at one point.

Measures and Instruments
To test the casual relationships between exogenous constructs and endogenous constructs, a scale (questionnaire) was developed by adapting the adopted scales through published literature on the study constructs. The questionnaire was presented to a group of academic specialists in supply chain management and business analytics, and the scale was modi ed according to their observations. The scale was then translated from English to the local language (Arabic) in Jordan to make it accessible to the highest possible number of participants. The questionnaire items were developed using a ve-point Likert scale to measure the participants' responses to the questionnaire items.  Table 1 shows the questionnaire items distributed to the study participants. The rm is constantly training employees to provide them with new environmental skills and knowledge.

GHC3
The rm's employees have good environmental service performance.

GHC4
The rm's employees work as a team when carrying out environmental work and activities within the rm.

GHC5
The rm's employees are considered environmentally better compared to competitors from other rms.

Green
Structural Capital

GSC1
The rm has an advanced management system to protect the environment. The rm is constantly spending on environmentally friendly facilities.

GSC3
The rm has e cient processes that achieve resource savings, leading to environmental protection.

GSC4
The rm applies knowledge management systems to share environmental knowledge among employees.

GSC5
The rm documents the environmental knowledge and experience of employees through databases.

GSC6
The rm documents intellectual property rights related to the environment (such as patents and software) as a way to store knowledge.

Green
Relational Capital

GRC1
The rm takes into consideration the environmental aspects of its customers when designing or manufacturing its products. Customers feel satis ed when the rm offers products of an environmentally friendly nature.

GRC3
The rm has long-term, environmentally focused, collaborative relationships with suppliers.

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Construct The rm has long-term, environmentally focused, collaborative relationships with customers.

GRC5
The rm actively cooperates with external parties to develop new environmental innovations or improve environmentally friendly ways of working.
Big Data Analytics Capabilities

BDAC1
The rm continuously invests in big data analysis software. The rm invests in technical infrastructure that includes information integration using advanced technology.

BDAC3
The rm invests in processes that ensure the availability of high-quality and timely data.

BDAC4
The rm's management attracts human resources with knowledge and experience in big data analytics.

BDAC5
The rm encourages employees to make use of their skills in big data analysis to solve various problems in creative ways.

BDAC6
The rm has administrative and organizational resources to take relevant actions on insights derived from big data analytics.
Green Supply Chain Performance

GSCP1
The rm's manufacturing system is energysaving. The rm's management encourages suppliers to improve environmentally oriented transportation processes continuously.

GSCP3
The rm's management provides continuous support and training to suppliers concerning environmental aspects and considerations.

GSCP4
The rm's management is interested in enhancing the communication level with its main customers and providing them with the rm's latest environmental developments.

GSCP5
The stock level has decreased over the past period.

GSCP6
The cost of purchasing materials has decreased over the past period.

The Study Population and Sample
This study was conducted in the manufacturing sector in Jordan. This sector was chosen for several reasons, the most important of which is the effects of the global COVID-19 pandemic on global supply chains (Joshi & Sharma, 2022), which has constituted a widespread disturbance in the sustainability of supply chains, and thus understanding how and strategies that rms in this sector can adopt to address the pandemic by achieving sustainability in the supply chain, in particular the green supply chain. The simple random sampling technique was adopted in the distribution of the questionnaire because this method provides equivalence and equality in the selection of the study sample (Sekaran & Bougie, 2016; Salkind & Rainwater, 2003), thus avoiding bias in the distribution process. The analysis unit included all the organizational and administrative units within these rms. The number of distributed questionnaires adopted for statistical analysis was 438. Table 2 shows the distribution of participants by demographic and personal characteristics.

Data Analysis And Results
This study aimed to investigate the causal relationships among several constructs because it contains several direct-in uencing hypotheses, mediating hypotheses and moderating hypotheses. Therefore, GHC2, GHC5, GSC1, GSC2, GSC5, GRC5 and BDAC6 were excluded. Table 3 summarizes the convergent validity and reliability of the constructs.

Structural Model
This study includes six hypotheses-three direct-in uencing hypotheses and three hypotheses that test the moderating role of big data analytics capabilities. The relationships between exogenous constructs and endogenous constructs were tested by the bootstrapping technique, which is a nonparametric technique provided by Smart-PLS (Streukens & Leroi-Werelds, 2016).
The results of the inner model test are summarized in Fig. 2. The gure shows estimates of paths and causal relationships between the components of green intellectual capital (green human capital, green structural capital and green relational capital) and the green supply chain performance with the presence of big data analytics capabilities as a moderating construct. The values of the path coe cients (β) as well as calculated t values and p values were used to evaluate the relationships between exogenous and endogenous constructs. A rule of thumb by which the hypothesis is accepted is that the calculated t value must be greater than 1.96 and the p value must be less than 0.05, and if the result is otherwise, the null hypothesis is accepted. Table 5 shows the results of hypotheses testing. The results supported the proposed study hypotheses (H1, H2 and H3) related to the direct effect of the components of green intellectual capital on green supply chain performance. All direct relationships were positive and statistically signi cant. The results of the H1 test, which explain the direct effect of green human capital on the green supply chain performance were β = 0.207, t = 3.620 and p = 0.000. The results of the H2 test, which explain the direct effect of green structural capital on the green supply chain performance were β = 193, t = 3.931 and p = 0.000), and the results of the H3 test, which explains the direct effect of green relational capital on the green supply chain performance, were β = 0.339, t = 6.385 and p = 0.000.
The study hypothesis related to the moderating role of big data analytics capabilities in the relationship between the components of green intellectual capital and green supply chain performance were tested.
The results showed that H6 was supported, but H4 and H5 were not supported because they did not have statistical signi cance because the p value was greater than 0.05. The effect of big data analytics capabilities on green supply chain performance was positive and statistically signi cant  Figure 3 shows that big data analytics capabilities improve the relationship between green relational capital and green supply chain performance. The value of R 2 was calculated as 0.717, which indicates a high explanation percent of the model and that the model has a high explanation quality (Hair et al., 2019); therefore, the value of the variance in endogenous constructs was 71.7%.

Discussion And Conclusion
The current study tested the causal relationships between green intellectual capital and green supply The study's results concerning the moderating role of big data analytics capabilities are noteworthy because H6 was accepted whereas both H4 and H5 were rejected. According to the experimental results, big data analytics capabilities played a moderating and positive role in the relationship between green relational capital and green supply chain performance. According to the RBV, rms' exploitation and incorporation of their unique resources into their activities enhance organizational performance (Hamdoun, 2020). From an NRBV viewpoint, sustainable performance can be improved through optimal resource utilization, particularly when considering environmental aspects (Hart, 1995). Thus, the use of industrial rms in Jordan for their green relational capital with big data analytics will enhance the green supply chain performance by collecting big data from key suppliers and customers, generating new knowledge and sharing it within the supply chain to improve the green supply chain performance With the importance of big data analytics capabilities in improving green supply chain performance (Liu et al., 2020), the empirical results revealed that there was no moderating role for big data analytics capabilities with green human capital and green structural capital on green supply chain performance.
Although previous literature has emphasized the importance of previous relationships and their role in improving the performance of the green supply chain, these results may differ among different contexts because the manufacturing sector in Jordan is considered an emerging sector. Therefore, rms may have medium or low capabilities in adopting big data analytics, or the process of collecting large data needs to be improved in a way that raises the quality level of this data.

Theoretical Implications
This study has numerous theoretical implications. The main contributions of this study were its examination of green management practices through the components of green intellectual capital on the green supply chain performance in the manufacturing sector in Jordan as well as its identi cation of the impact of big data analytics capabilities as a moderating role in these relationships. Although several studies have examined the relationship of intellectual capital with supply chain performance according to big data analytics, this study is one of the few studies that focused on the environmental aspect of these relationships and the extent to which these rms bene t from big data analytics capabilities. Based on the RBV and NRBV, the study con rmed what previous studies have shown, which is that focusing on organizational efforts to build strong green intellectual capital enhances supply chain sustainability and improves green supply chain performance. However, this study further highlighted the existence of a clear and signi cant role for green relational capital in the green supply chain performance. Furthermore, this study provides important results for academics by shedding more light on social capital and its relationship with environmental management and sustainability in the future.
This study also provided a new understanding of how big data analytics capabilities can improve the relationship between green relational capital and green supply chain performance. Big data analytics capabilities help companies respond effectively in real-time data processing (Wang et al., 2018).
Integrating these capabilities to increase cooperation with suppliers and customers could, furthermore, improve green supply chain performance. However, some results of the study were inconsistent with the previous literature. Speci cally, the results revealed a lack of statistical signi cance for the interaction of big data analytics capabilities, green structural capital and green human capital on green supply chain performance.

Managerial Implications
The study highlighted various implications for administrators and supply chain managers in the manufacturing sector in Jordan. The study results con rmed that all components of green intellectual capital had a positive impact on the green supply chain performance. Therefore, managers should invest more in developing the skills and experience of their employees to improve their green capital, thereby enhancing the improvement of the level of sustainability in the supply chain. Managers in the Jordanian manufacturing sector should also invest in green technology and develop environmentally friendly databases. Also, managers should pay more attention to organizational procedures and policies that encourage the protection of the environment. This can be done by setting regulations and rules that encourage workers to think creatively regarding the environment and document these innovations in various storage systems. The study's results con rmed that green relational capital is more strongly linked to the green supply chain performance than other components. Therefore, managers should build green relational capital with key suppliers and consistently share information to ensure the implementation of environmental initiatives and programs. Also, managers should provide adequate training for employees to increase their environmental awareness and also build mutual trust among employees to improve green internal integration in the green supply chain. Managers can additionally bene t from holding formal and informal meetings to increase the participation of environmental objectives with employees and even with suppliers, thus increasing environmental awareness and enhancing the green supply chain performance. The study suggests increasing managers' focus in supply chain management in the Jordanian manufacturing sector on increasing cooperation with customers by involving customers in setting the rm's environmental goals and allowing them to put their suggestions and ideas into action within the green supply chain.
The study's results revealed that big data analytics capabilities are positively related to green supply chain performance and that these capabilities moderate the positive relationship between green relational capital and green supply chain performance. Managers in this sector should enhance the technological and organizational capabilities that contribute to adopting big data analytics by investing in infrastructure and metadata and training employees in the latest technologies. In addition, managers can further bene t from big data analytics capabilities by using more than one method of data collection, such as both quantitative and qualitative data, and using social media to collect data and provide a database dedicated to this process. The study recommends managers in human resources departments in the manufacturing sector in Jordan develop incentive systems dedicated to big data initiatives to motivate employees to exploit big data technologies to create added value for the green supply chain.
The study additionally recommends that managers use arti cial intelligence techniques and social media to create accurate predictive models regarding customer preferences. These practices enhance the creation of added value and thus allow for high performance in the green supply chain.

Limitations and Future Research
Although this study had noteworthy results, it is subject to many limitations, which must be taken into consideration when generalizing the results of the study. First, the study data were collected through cross-sectional data, but it would be useful to understand the relationship among the study's constructs by using longitudinal data or panel data. Second, this study was conducted in the context of the manufacturing sector in Jordan, so it would be useful to conduct studies in different contexts, countries and cultures to enable scholars and management practitioners to better understand the relationships between the components of green intellectual capital and green supply chain performance. Third, the quantitative data collected through the questionnaire were relied on to test the hypotheses and objectives of the study, but qualitative methods such as interviews were ignored when conducting the study. Thus, it would be interesting to conduct future studies based on a qualitative approach. Finally, the study suggests conducting future studies that focus on mediating constructs such as green innovation or green human resource management practices and examining more causal relationships of these constructs.   Big data analytics moderates the relationship between green relational capital and green supply chain performance