Measuring the Environmental Maturity of the Supply Chain Finance: A Big Data-Based Multi-Criteria Perspective

This paper presents a strategic roadmap to handle the issue of resource allocation among the green supply chain management (GSCM) practices. This complex issue for supply chain stakeholders highlights the need for the application of supply chain finance (SCF). This paper proposes the five Vs of big data (value, volume, velocity, variety, and veracity) as a platform for determining the role of GSCM practices in improving SCF implementation. The fuzzy analytic network process (ANP) was employed to prioritize the five Vs by their roles in SCF. The fuzzy technique for order preference by similarity to ideal solution (TOPSIS) was then applied to evaluate GSCM practices on the basis of the five Vs. In addition, interpretive structural modeling (ISM) was used to visualize the optimum implementation of the GSCM practices. The outcome is a hybrid self-assessment model that measures the environmental maturity of SCF by the coherent application of three multicriteria decision-making techniques. The development of the Basic Readiness Index (BRI), Relative Readiness Index (RRI), and Strategic Matrix Tool (SMT) creates the potential for further improvements through the integration of the RRI scores and ISM results. This hybrid model presents a practical tool for decision-makers.


Introduction
In industrial economics, "Everything is worth what its purchaser will pay for it" (Publilius Syrus, 1st Century B.C., cited in [1]). Unfortunately, this is not reflected in current green supply chain management (GSCM) practices. Indeed, the cost of GSCM implementation is greater than the expected return [2,3]. The main reason for this is the cost of changing practices, including those surrounding human resources [3], and adopting new green systems [4]. Consequently, it is not surprising that financial barriers represent one of the main obstacles to GSCM implementation [2,3,5,6].
It must be noted that the costs of being green are associated with not only environmental elements but also organizational functions [7]. Hervani et al. [5]  Specifically, linking environmental-related strategic purchasing activities to supply chain management (SCM) practices facilitates GSCM implementation [8]. This, in turn, creates new dimensions for sophisticated networks of buyers and sellers in various industries [5,9]. This complex situation for supply chain stakeholders highlights the need for the implementation of supply chain finance (SCF), a solution developed by academicians and practitioners.
SCF has been defined as a facilitator of the physical and information flows of the financial products and services provided by financial institutions [10]. It is the ability to optimize supply chains to enable financial infrastructures and cash flows [11]. Wuttke et al. [12] defined SCF as cash flow optimization with respect to planning, management, and control to improve material flows. Other studies have offered similar definitions [13][14][15][16][17][18]. These end of Section 2. The employed methods and contexts of their application are presented in Section 3. Section 4 provides the results of the proposed model. The implications, contributions, and directions for future studies are presented in Section 5. Finally, Section 6 presents the conclusion for this paper. A list of acronyms and their descriptions are presented in Table 1.

Top Management Commitment
Top management commitment (TOPM) is a crucial driver for greening SCM. Top and senior management commitment is required to implement environmental management systems that are in harmony with other green practices and facilitate the monitoring of the organization's environmental progress [39]. Management commitment also enhances internal cross-functional collaborations among operational units. This may include empowering employees [40], implementing effective reward systems, providing training, and promoting teamwork [41]. Therefore, management commitment is important in environmental initiatives [37,42]. For example, managers must be committed to applying GSCM to purchasing, such as the reuse and recycling potential of purchased products and materials [43][44][45][46][47][48]. Indeed, studies have emphasized the importance of top management support to the success of organizational initiatives, including those related to GSCM [37].

Organizational Interaction Maturity
Organizational theory has undergirded the exploration and elucidation of GSCM [49]. According to resource-based theory, organizational infrastructure consists of a combination of resources and capabilities that create a competitive advantage [50]. The effective utilization of these resources leads to the successful implementation of competitive strategies, including those related to the environment [37,51]. Therefore, organizational learning enhances the implementation of sophisticated systems, such as GSCM [37,[52][53][54][55][56].

Quality Management
Quality management (QUAL) is the key to organizational performance. It also plays a significant role in organizational environmental practices [57][58][59], which lead to better GSCM practices [60][61][62]. Jabbour et al. [62] investigated the role of QUAL, environmental management maturity (EMM), and other GSCM practices in green performance. Their study of 95 Brazilian companies found that it is essential to organizational EMM, which can improve green performance, especially in purchasing. Other studies have also emphasized the significance of QUAL in improving GSCM practices [63][64][65][66][67].

Environmental Management
Waste and costs can be efficiently minimized, and better environmental performance can be achieved when leading companies practice GSCM [68]. Thus, ISO 14001 plays a significant role in enhancing GSCM. In a study on the effects of ISO 14001 certification in Japanese facilities, Arimura et al. [69] concluded that these standards encouraged GSCM practices. Other studies have drawn similar conclusions [70][71][72][73].

Customer Relationship Management
One of the main elements of supply chain operations is stakeholder collaboration in the development of environmental goals [61,63,74]. Thus, customer pressure plays a Logistics 2021, 5, 22 5 of 24 significant role in GSCM [75][76][77]. Thun and Müller [76] found that GSCM practices in German manufacturing were driven by customer pressure; consequently, good customer relationships were considered as a competitive advantage. Currently, the application of green requirements to the supply chain constitutes a competitiveness criterion for evaluating firm performance [77]. Several studies have concluded that customer relationship management is key to the implementation of GSCM practices [65,67,[78][79][80].

Green Human Resource Management
Human resource management (HRM) practices encourage the creation of green organizations [81]. Cantor et al. [82] developed a model to investigate the relationship between organizational environmental initiatives and HRM practices. The key features of green training related to green human resource management (GHRM) and GSCM practices in Brazilian organizations have been investigated [56]. Jabbour and de Sousa Jabbour [83] observed that GHRM and GSCM play a significant role in creating a sustainable workplace environment; however, further research is needed. Therefore, an assimilation framework for GHRM-GSCM interactions was proposed. Muduli et al. [84] used ISM to investigate the relationships among GCSM-related behaviors. To address these complex organizational environmental challenges, GHRM must be considered because of its contribution to employee environmental authorizations [85], a green organizational culture [86,87], and the enhancement of environmental groups [85,88].

Smart Process and Design
Smart process and design (SP&D) considers product eco-design and reverse logistics. Eco-design refers to the consideration of stakeholder needs in the environmental elements of product design and development [99][100][101], including sustainable packaging [102]. Reverse logistics describes the production activities related to the three "REs" of raw materials: reduction, reuse, and recycling [99,103,104]. SP&D covers a wide range of successful GSCM practices, such as purchasing equipment that produces clean products, selling obsolete stock, selling waste materials, and reducing energy consumption [38].

MCDM Applications in GSCM
In this regard, several MCDM studies have addressed GSCM. Although many studies noticed that the lack of MCDM applications is still considered as a research gap, specifically in the field of reverse logistics [105], Vieira et al. [106] concluded that several MCDM methods have the potential to investigate several issues related to GSCM such as the prioritizing of the reverse logistics barriers. Kechagias et al. [107] aimed at creating a process reference model and have developed an analytic hierarchy process (AHP)-based Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) model to propose a systemic methodology for selecting the best Business Process Modeling (BPM) tools. They concluded that the proposed model is capable of contributing to improvement of sustainability in the context of SCM. Büyüközkan and Çifçi [108] highlighted the importance of the environmental dimension in achieving financial success. Accordingly, they developed a fuzzy-based ANP model to evaluate GSCM practices in an evolved Turkish company. Büyüközkan and Çifçi [91] emphasized the relationship between environmental performance and internal and external factors. Thus, they developed an MCDM model that employed fuzzy ANP and TOPSIS to optimize supplier selection. Wang and Chan [109] developed a fuzzy TOPSIS model to increase practitioners' and decision-makers' under- standing of the resources that are needed as a consequence of the greening of SCM practices. A fuzzy TOPSIS model was developed to enable a Brazilian company to optimize electronics supplier selection [38]. ISM has been employed as a research method in many fields, including GSCM [110][111][112][113]. For example, Mathiyazhagan et al. [111] used sophisticated ISM to examine 26 barriers to GSCM in Indian automobile component manufacturing. Further technical explanations of the procedures for performing fuzzy ANP, fuzzy TOPSIS, and ISM are presented in succeeding sections of the paper.

Methods and Applications
Three MCDM methods were employed: fuzzy ANP, fuzzy TOPSIS, and ISM. The employment of these methods can be justified by looking at the three research questions separately and collectively. RQ1 aims to investigate the interactions among the "five Vs. of big data" to improve SCF practices. Such interactions imply the existence of "dependency" and "feedback" relationships among the "five Vs". The ANP, in particular, is a best fit tool that can discover, handle, and quantify such relationships [114,115]. This point has also been discussed and validated in many ANP-based research works [116][117][118]. RQ2 aims to evaluate eight different GSCM practices with respect to the five criteria (i.e., the five Vs). This is a typical MCDM problem that is can be resolved by TOPSIS and, academically, TOPSIS represents the most commonly acceptable MCDM tool to handle such an issue, particularly, in the fields of SCM and the environment [119]. Note that both ANP and TOPSIS were carried out with the aid of the fuzzy set theory because the fuzzy-based ANP and TOPSIS reduce the effect of subjectivity [120,121]. Regarding RQ3, optimal resource mobilization cannot be attained unless the eight GSCM practices receive the entitled priority in terms of which practice should be implemented before the other one. Such a special configuration of a decision-making situation requires a tool that can handle the complexity amongst the investigated practices [106]. Hence, in this regard, ISM was found to be a better application because it has been widely validated as an effective decision-making method and, specifically, as a tool to handle the complexity issue [122]. Collectively, the sequence the three techniques are applied in creates a hybrid self-assessment model to measure the environmental maturity of the SCF (Figure 1).

Fuzzy Analytic Network Process
The ANP is the generalized form of the analytic hierarchy process (AHP), a wellknown MCDM technique [115]. Consequently, the AHP and fuzzy AHP can be considered special ANP and fuzzy ANP cases. Accordingly, the fuzzy ANP has been used as an extension of the traditional fuzzy AHP applications [123,124]. In AHP and fuzzy AHP, groups of elements are organized in a hierarchal structure. The application of the

Fuzzy Analytic Network Process
The ANP is the generalized form of the analytic hierarchy process (AHP), a wellknown MCDM technique [115]. Consequently, the AHP and fuzzy AHP can be considered special ANP and fuzzy ANP cases. Accordingly, the fuzzy ANP has been used as an extension of the traditional fuzzy AHP applications [123,124]. In AHP and fuzzy AHP, groups of elements are organized in a hierarchal structure. The application of the ANP and fuzzy ANP facilitates the formulation of more complex relationships through sets of clusters (network-based structure). These ideas have been explored in several studies [125][126][127].
In the ANP, a numerical scale is used to make sets of judgments, and in the fuzzy ANP, a linguistic scale is used. The MCDM literature provides several approaches to the implementation of the fuzzy AHP and ANP [128][129][130][131][132][133][134][135][136]. However, most of these approaches are complex. Hence, Chang's extent analysis has been considered the most accepted method because of its simplicity [124,137,138]. The present study employed Chang's extent analysis, which has been applied in previous studies [138,139].
To implement Chang's extent analysis, let Q symbolize the object set such that Q = ..  q m and R symbolizes the goal set such that R = {r 1 , r 2 , . . . , r n }. This application of Chang's extent analysis implies that the objects are considered independently in a sequential approach. In addition, for each independent object (i.e., ..  q m ), the analysis is executed for each goal, r j . Accordingly, n numbers of extent analyses are executed as follows: where F i r j , (i = 1, 2, . . . , n) symbolizes the triangular fuzzy numbers. F (x) symbolizes the membership function of the triangular fuzzy number. According to Erensal et al. [137], Chang's extent analysis can be illustrated in four steps: Step 1: For each object j, the fuzzy synthetic extent can be expressed as: As expressed in Equation (2), ⊗ symbolizes the extended multiplication of two fuzzy numbers. To perform n ∑ i=1 F i rj , n extent analysis values are executed such that: can be obtained by performing the fuzzy addition operation of F i r j , (i = 1, 2, . . . , n) as shown in Equation (4): The inverse of the vector can then be obtained: where p j , n j , h j > 0. After that, the C j can eventually be obtained such that: Step 2: The opportunity that [ can be expressed as: Figure 2 illustrates all the cases of V F 2 ≥ F 1 . For example, in the case of w 2 > k 1 > t 2 > w 1 , point z represents the value that matches the highest intersection point of The inverse of the vector can then be obtained: where , , ℎ > 0. After that, the can eventually be obtained such that: Step 2: The opportunity that [ = (ℎ , , ) ] ≥ [ = (ℎ , , ) ] can be expressed as:   Step 3: The chance of the occurrence of a convex fuzzy number greater than y convex fuzzy numbers F j (j = 1, 2, . . . , y) can be expressed as follows: Step 4: The last step is to find the weight vector for y = 1, 2, . . . , m such that:

Fuzzy TOPSIS
The fuzzy TOPSIS was developed by Chen [140] to address the issue of uncertainty in MCDM problems. With the fuzzy TOPSIS, decision-makers, DM r , (r = 1, . . . , k) use linguistic terms to rate the criteria and alternatives. Accordingly, w r j represents the weight of the criterion j assigned by the DM r such that C j , ( j = 1, . . . , m). Correspondingly, x r ij represents the weight of the alternative i with respect to C j assigned by the DM r , such that A i , ( i = 1, . . . , n). Several studies [38,141] have summarized the fuzzy TOPSIS as follows:

1.
Aggregating the weights of the criteria and alternatives collected from k decisionmakers, as shown in Equations (10) and (11): 2.
Constructing the fuzzy decision matrix of the criteria ( D) and the alternatives ( W), as shown in Equations (12) and (13): 3.

R) can be expressed as indicated in Equations
..
where the distances between two triangular fuzzy numbers e and f are calculated according to the vertex method shown below: .. 7.
Finding the closeness coefficient ..
Ranking the corresponding alternatives according to the value of ..

CC i .
Accordingly, the closest alternative to the FPIS is considered the best alternative.

Interpretive Structural Modeling
Several studies have discussed the mathematical foundation of ISM [142][143][144]. The philosophical background was presented by Warfield [145]. ISM is a mathematical technique to simplify the multifaceted relationships of any group of elements to develop a meaningful model for decision-makers [146]. It illustrates and explains the relationships among groups of entities, such as factors, elements, practices, barriers, and initiatives [111,[146][147][148][149][150]. On the basis of applications in the literature [147,150,151], the steps in ISM are as follows: 1.
Identifying the practices, such as factors, initiatives, barriers, or elements, to be examined.

2.
Describing the contextual relationships among the sets of practices through four symbols:

3.
Forming the structural self-interaction matrix (SSIM) as a result of the pair-wise contextual relationships among the examined practices.

4.
Extracting the reachability matrix from the SSIM, which is converted into an initial reachability matrix in accordance with the following replacement rules: • If the (α, β) entry is V, then the (α, β) entry in the reachability matrix becomes 1, and the (β, α) entry becomes 0. • If the (α, β) entry is A, then the (α, β) entry in the reachability matrix becomes 0, and the (β, α) entry becomes 1.

5.
The initial reachability matrix then has to be tested for transitivity to ensure that if practiceÄ leads to the implementation of practiceË, and practiceË leads to the implementation of practiceÜ, then practiceÄ leads to practiceÜ. The resulting matrix is referred to as the final reachability matrix. 6.
Based on the final reachability matrix, the set of practices can be classified into four categories: driving, linkage, dependent, and autonomous. 7.
Based on the final reachability matrix, the set of practices can also be hierarchized. The final diagraph can then be developed in accordance with the identified levels.

The Application of Fuzzy ANP
Outer-and inner-dependence relationships were considered in the proposed fuzzy ANP model. As shown in Figure 3, the arrow from the SCF cluster to the big data cluster indicates the outer-dependence relationship in that all five Vs were weighted by their contributions to SCF practices. The arrows that connect the five Vs inside their cluster represent the inner-dependence relationships among the five Vs.
implementation of practice Ȕ, then practice Ȁ leads to practice Ȕ. The resulting matrix is referred to as the final reachability matrix. 6. Based on the final reachability matrix, the set of practices can be classified into four categories: driving, linkage, dependent, and autonomous. 7. Based on the final reachability matrix, the set of practices can also be hierarchized.
The final diagraph can then be developed in accordance with the identified levels.

The Application of Fuzzy ANP
Outer-and inner-dependence relationships were considered in the proposed fuzzy ANP model. As shown in Figure 3, the arrow from the SCF cluster to the big data cluster indicates the outer-dependence relationship in that all five Vs were weighted by their contributions to SCF practices. The arrows that connect the five Vs inside their cluster represent the inner-dependence relationships among the five Vs. For the application of the fuzzy ANP model, the opinions of experts from one of the international leading food and retail holding companies were solicited. Accordingly, a group of experts in the field of SCM, including the supply and demand director, were selected. For this study, all selected experts were well-educated, holding critical managerial positions, and capable of dealing with various SCM issues concerning different contexts. To conduct fuzzy ANP and fuzzy TOPSIS, two different questionnaires were presented to the experts within several sequence sets of meetings until the consensus For the application of the fuzzy ANP model, the opinions of experts from one of the international leading food and retail holding companies were solicited. Accordingly, a group of experts in the field of SCM, including the supply and demand director, were selected. For this study, all selected experts were well-educated, holding critical managerial positions, and capable of dealing with various SCM issues concerning different contexts. To conduct fuzzy ANP and fuzzy TOPSIS, two different questionnaires were presented to the experts within several sequence sets of meetings until the consensus was reached. For fuzzy ANP, experts' views on the significance of each "V" in improving the implementation of SCF were sought. For example, they were asked about the importance of data velocity vs. data value in the improvement of SCF practices. All the Vs were compared on the fuzzy ANP linguistic scale (Table 3). It is worth mentioning herein that, according to Yin [152], the outcome of any case study can be validated analytically (i.e., analytical generalization) either by replication logic, in the case of having multiple case studies, or by theory development, as conducted in the current single case study (i.e., decision-making theory). Hence, Yin emphasized that the focus should be on the case study as an "opportunity to shed empirical light on some theoretical concepts or principles . . . that is, analytic genaralization" regardless of the number of cases. This is completely different from the traditional statistical validation of most quantitative research works. Further details regarding case study validation using the approach of analytical generalization can be found in [152]. Additionally, most of the ANP-based case studies have been validated and generalized analytically, not statistically, with small sample sizes [118,153].

The Application of Fuzzy TOPSIS
The extracted weights generated by the fuzzy ANP were considered in the development of the fuzzy TOPSIS model. The five Vs facilitated the rating of GSCM practices on the basis of their contributions to SCF practices. Experts used the fuzzy TOPSIS scale to evaluate each GSCM practice on each criterion (Figure 4). For example, the following question was posed to the experts: to what extent are the QUAL-related data as a GSCM practice "big" enough to improve the data flows to facilitate the SCF implementation? As indicated in Figure 4, the corresponding linguistic rates for answering the question were "big enough", "big", "normal", "partially incomplete", and "incomplete." All GSCM practices can be rated on the corresponding linguistic fuzzy TOPSIS scale (Figure 4).

The Application of ISM
The ISM approach was employed to prioritize the GSCM practices by their reciprocal influence. Specifically, ISM was performed to identify the GSCM practices that should be implemented first regardless of their importance, criticality, or readiness scores. ISM achieved two significant outcomes: 1. classification of GCSM practices into four categories: independent (drivers or driving practices), linkage, dependent, and autonomous practices; 2. identification of the influence of each GSCM practice.
Further explanations of the proposed indices (i.e., the integration of the fuzzy TOPSIS and ISM outcomes) and key results are presented in the next section.

The Application of ISM
The ISM approach was employed to prioritize the GSCM practices by their reciprocal influence. Specifically, ISM was performed to identify the GSCM practices that should be implemented first regardless of their importance, criticality, or readiness scores. ISM achieved two significant outcomes: classification of GCSM practices into four categories: independent (drivers or driving practices), linkage, dependent, and autonomous practices; 2.
identification of the influence of each GSCM practice.
Further explanations of the proposed indices (i.e., the integration of the fuzzy TOPSIS and ISM outcomes) and key results are presented in the next section. Table 4 illustrates the contribution of each V in terms of facilitating SCF implementation. The inner-dependence relationships among the Vs, the outer-dependence linkage with SCF, and the overall significance weights that resulted from the application of the fuzzy ANP were considered. Regarding the inner-dependence relationships among the five Vs, Table 4 indicates that three (veracity, variety, and volume) of the four criteria contributed equally (31%) to the valuableness of the SCF data. However, data velocity was considered the least significant criterion (6%). Similarly, with respect to the veracity of the SCF data, all the criteria exhibited the same significance level (31%). The exception was velocity, which was considered to be relatively unimportant (6%). The velocity of the SCF data was found to be dependent mainly on the variety (40%) and volume (40%). Data veracity and value had the same low contribution level (10% on each criterion). Similarly, with respect to the volume of the data, data veracity and value were considered less important (10% on each criterion) while data velocity (40%) and variety (40%) were considered to be relatively important. Moreover, it can also be seen that the variety of the SCF data was dependent mainly on the veracity (53%) and velocity (43%). Data value and volume were considered insignificant (2% on each criterion). With respect to the outer-dependence linkage with SCF, data veracity and value were scored similarly (44%). Velocity, variety, and volume were scored as 3%, 4%, and 5%, respectively. The overall scores for the significance weighting of data veracity, value, velocity, variety, and volume were 30%, 29%, 7%, 17%, and 17%, respectively.  Table 5 presents the final results for the fuzzy TOPSIS model. It can be observed that the highest score was recorded for SP&D (0.145). This indicates that it is the most mature GSCM practice for improving the implementation of SCF. The scores for the remaining GSCM practices were 0.131, 0.104, 0.101, 0.098, 0.081, 0.072, and 0.064 for QUAL, SUPP, CUTS, organizational interaction maturity (ORGM), environmental management (ENVI), TOP, and GHRM, respectively.

Basic Readiness and Relative Readiness Indices
All GSCM practices were rearranged by their final fuzzy TOPSIS scores (Table 6) with additional interpretations. Thus, two indices were developed: the Basic Readiness Index (BRI) and the Relative Readiness Index (RRI). The BRI measures the actual readiness of each GSCM practice to facilitate the SCF implementation by normalizing the final fuzzy TOPSIS scores (CCi). With the BRI, the final fuzzy TOPSIS score (CCi) for each GSCM practice is divided by the sum of the scores for all the GSCM practices ( Table 6). The SP&D score on the BRI was 18.2% ([0.145/0.796] × 100 = 18.2%). This represented the highest normalized fuzzy TOPSIS score and, therefore, the highest BRI. GHRM had the lowest BRI score (8.0%). The results indicate that the GSCM practices were generally not ready for SCF implementation because the BRI scores were between 8.0% and 18.2%. However, this is different from the interpretation through the RRI. The RRI measures the relative readiness of each GSCM practice to facilitate SCF implementation by benchmarking the final fuzzy TOPSIS scores (CCi). With the RRI, the final fuzzy TOPSIS score (CCi) for each GSCM practice is divided by the highest score (Table 6). For example, the RRI score of 69.4% ([0.101/0.145] × 100 = 69.4%) for customer relationship management (CUST) represents the benchmarked fuzzy TOPSIS score because it reflects the relative readiness of CUST compared with the best practice (SP&D). Accordingly, the RRI score for SP&D was 100% ([0.145/0.145] × 100 = 100%), thereby indicating that SP&D was the best practice (compared to all other practices). Consequently, GHRM had the lowest RRI score (44.1%). The outcomes of the integration of the RRI scores and ISM results are discussed in the next sections.  Table 7 shows the computed driving power and dependence power for each GSCM practice. It shows that SUPP, ENV, and QUAL had the same driving power (5) and a similar dependence power (8). CUST also had a similar driving power (5) but slightly different dependence power (7). Likewise, GHRM and ORGM had similar driving power (7) and similar dependence power (3). The TOPM scores indicate the highest driving power (8) and the lowest dependence power (1). In contrast, the SP&D scores indicate the lowest driving power (4) and the highest dependence power (8). On the basis of the computed driving power and dependence power, the GSCM practices were placed into three categories: driver (independent element zone), linkage, and dependent element zones ( Figure 5). TOPM, GHRM, and ORGM are located in the driver zone and SP&D is considered an a dependent practice. CUST, SUPP, ENVI, and QUAL are considered linkage practices because they have relatively high driving and dependence power. The final ISM is the result of four sophisticated iterations ( Figure 6). Figure 7 illustrates the final ISM in which TOPM represents the basis for the efficient implementation of the other GSCM elements.

ISM Results
In other words, the successful application of the GSCM-related efforts and resources are dependent on TOPM assurances and implementation. In contrast, SP&D is considered to result from the implementation of the other GSCM practices. Thus, the effort and resources needed for the implementation of SP&D cannot be technically justified. The application of the same logic and argument to all GSCM practices to improve the SCF environment ( Figure 7) represents an effective initial GSCM implementation roadmap that considers the prioritized levels corresponding to each set of practices.

Implications, Contributions, and Directions for Future Studies
Synergized outcomes can be extracted when ISM results are interpreted by RRI scores or vice versa. In Figure 8, which is an adjustment of Figure 5, the size of the bubble is a reflection of the corresponding RRI score for each GSCM practice. It indicates that the drivers (independent practices) are less ready for facilitating SCF implementation than the linkage practices or the sole dependent practice-i.e., SP&D. For example, TOPM was considered as a driver. The corresponding RRI score was very low (50%). This indicates that TOPM lacked sufficient readiness or maturity to enhance SCF. Nevertheless, it was assumed to be the most ready practice because of the corresponding results for its driving power (8-i.e., high), dependence power (1-i.e., low), and phase or priority level (Phase 1). In contrast, SP&D was considered a dependent practice. The corresponding RRI score was the highest (100%). This indicates that SP&D was the readiest practice. However, this was counter to its corresponding driving power (4), which was the lowest; dependence power (8), which was the highest (i.e., = 8); and phase or priority level, which was the lowest (Phase 4).

Implications, Contributions, and Directions for Future Studies
Synergized outcomes can be extracted when ISM results are interpreted by RRI scores or vice versa. In Figure 8, which is an adjustment of Figure 5, the size of the bubble is a reflection of the corresponding RRI score for each GSCM practice. It indicates that the drivers (independent practices) are less ready for facilitating SCF implementation than the linkage practices or the sole dependent practice-i.e., SP&D. For example, TOPM was considered as a driver. The corresponding RRI score was very low (50%). This indicates that TOPM lacked sufficient readiness or maturity to enhance SCF. Nevertheless, it was assumed to be the most ready practice because of the corresponding results for its driving power (8-i.e., high), dependence power (1-i.e., low), and phase or priority level (Phase 1). In contrast, SP&D was considered a dependent practice. The corresponding RRI score was the highest (100%). This indicates that SP&D was the readiest practice. However, this was counter to its corresponding driving power (4), which was the lowest; dependence power (8), which was the highest (i.e., = 8); and phase or priority level, which was the lowest (Phase 4). To overcome this issue, the present study developed a strategic matrix tool (SMT), which is important for the reorganization and remobilization of the available resources. In Figure 9, the prioritized phases are listed vertically by the corresponding GSCM practice categories (zones). This also illustrates the horizontal classification of the RRI scores into three categories: 1, RRI score ≤ 50%; 2, RRI score ≥ 50% up to 90%; and 3, RRI score = 90% up to 100%. Therefore, three strategies were developed. The first (Strategy 1) refers to the process of increasing the allocated resources for each GSCM practice located in any square labeled "very high", "high", or "normal" attention in Phases 1 and 2 ( Figure  9). The second (Strategy 2) refers to the decrease in the allocated resources for each GSCM practice located in any square labeled "very high", "high", or "normal" attention in Phases 3 or 4. The third (Strategy 3) is the maintenance of the same level of resources for each GSCM practice located in any square labeled "low" attention in Phases 1, 2, 3, or 4. To overcome this issue, the present study developed a strategic matrix tool (SMT), which is important for the reorganization and remobilization of the available resources. In Figure 9, the prioritized phases are listed vertically by the corresponding GSCM practice categories (zones). This also illustrates the horizontal classification of the RRI scores into three categories: 1, RRI score ≤ 50%; 2, RRI score ≥ 50% up to 90%; and 3, RRI score = 90% up to 100%. Therefore, three strategies were developed. The first (Strategy 1) refers to the process of increasing the allocated resources for each GSCM practice located in any square labeled "very high", "high", or "normal" attention in Phases 1 and 2 ( Figure 9). The second (Strategy 2) refers to the decrease in the allocated resources for each GSCM practice located in any square labeled "very high", "high", or "normal" attention in Phases 3 or 4. The third (Strategy 3) is the maintenance of the same level of resources for each GSCM practice located in any square labeled "low" attention in Phases 1, 2, 3, or 4. The strategies for each practice are assigned on the basis of their location in the SMT (Figure 9). The strategies for each practice are assigned on the basis of their location in the SMT (Figure 9). This study contributes to the context of MCDM and its applications in GSCM from different angles. From a technical point of view, in order to evaluate the eight GSCM practices, fuzzy TOPSIS was conducted innovatively by using the relevant linguistic expression corresponding to to each V of big data (i.e., each criterion), as shown in Figure  4, which is a relatively more appropriate and accurate application compared to several traditional applications of fuzzy TOPSIS. Additionally, the study succeeded in visualizing the resources throughout a developed version of the ISM's final structure as shown in Figure 8. Such an innovative representation facilitates the resource allocation adjustment process to ensure better utilization of resources by each GSCM practice. Specifically, note that the size of the bubble is a reflection of the dedicated resources for each GSCM practice, which is a developed and unique form of ISM results representation.
Such technical contributions lead the talk to the practical/managerial contributions of the developed model. With such innovative tools proposed herein (i.e., BRI, RRI, and SMT), decision-makers can precisely allocate the required resources for the "demanding" GSCM practices with respect to the appropriate phase (i.e., timing). This can be performed by reducing the amount of resources dedicated to the GSCM practices in which resources are overutilized (i.e., saturated practices). Such strategic managerial actions can be executed with the aid of the three proposed strategies, as illustrated in the proposed SMT ( Figure 9). By following such an approach, practitioners can allocate more resources confidently to the independent GSCM practices (drivers) such as TOPM, GHRM, and ORGM because the current dedicated resources for these practices are below their requirements as "drivers". Similarly, resources can be deducted confidently from the dependent GSCM practices such as QUAL, SP&D, ENVI, CUST, and SUPP. Such MCDMdriven practical/managerial approaches for resource allocation and/or strategy creation are applicable in many fields including sustainability [154][155][156][157].
In this study, opinion-based measures for big data were employed when carrying out fuzzy ANP and fuzzy TOPSIS. However, the employment of a data-driven approach This study contributes to the context of MCDM and its applications in GSCM from different angles. From a technical point of view, in order to evaluate the eight GSCM practices, fuzzy TOPSIS was conducted innovatively by using the relevant linguistic expression corresponding to to each V of big data (i.e., each criterion), as shown in Figure 4, which is a relatively more appropriate and accurate application compared to several traditional applications of fuzzy TOPSIS. Additionally, the study succeeded in visualizing the resources throughout a developed version of the ISM's final structure as shown in Figure 8. Such an innovative representation facilitates the resource allocation adjustment process to ensure better utilization of resources by each GSCM practice. Specifically, note that the size of the bubble is a reflection of the dedicated resources for each GSCM practice, which is a developed and unique form of ISM results representation.
Such technical contributions lead the talk to the practical/managerial contributions of the developed model. With such innovative tools proposed herein (i.e., BRI, RRI, and SMT), decision-makers can precisely allocate the required resources for the "demanding" GSCM practices with respect to the appropriate phase (i.e., timing). This can be performed by reducing the amount of resources dedicated to the GSCM practices in which resources are overutilized (i.e., saturated practices). Such strategic managerial actions can be executed with the aid of the three proposed strategies, as illustrated in the proposed SMT ( Figure 9). By following such an approach, practitioners can allocate more resources confidently to the independent GSCM practices (drivers) such as TOPM, GHRM, and ORGM because the current dedicated resources for these practices are below their requirements as "drivers". Similarly, resources can be deducted confidently from the dependent GSCM practices such as QUAL, SP&D, ENVI, CUST, and SUPP. Such MCDM-driven practical/managerial approaches for resource allocation and/or strategy creation are applicable in many fields including sustainability [154][155][156][157].
In this study, opinion-based measures for big data were employed when carrying out fuzzy ANP and fuzzy TOPSIS. However, the employment of a data-driven approach would provide more accurate interpretations. To illustrate this, instead of using linguistic terms in measuring the GSCM practices with respect to the five Vs (Figure 4), the volume of data, for example, can be measured in Terabytes (TB) or even Petabytes (PB), the velocity of the data can also be measured, for example, in TB per second (TB/s), and so. Another direction for future research can be investigated within the context of resource utilization. Specifically, the required resources for each GSCM practice can be cascaded into a further levels of details such as the types of resources to be utilized (human, technical, organizational, physical, or financial), as discussed and illustrated in [118].

Conclusions
This study proposes a hybrid self-assessment model to measure the maturity of SCF considering the environmental dimension by the coherent application of three MCDM techniques: fuzzy ANP, fuzzy TOPSIS, and ISM. The five Vs of big data were found to provide a template for assessing of GSCM practices. The fuzzy ANP illustrates the quantification of the five Vs by considering their inner-and outer-dependence. The fuzzy TOPSIS measures the contribution of each GSCM practice to supply chain financial performance. Moreover, rather than extracting the final GSCM practice scores, as is typical of traditional fuzzy TOPSIS applications, the study developed two indices: the BRI and RRI. The BRI scores indicated that the GSCM practices in the investigated company were not mature enough to improve the implementation of SCF. However, the proposed RRI index facilitated the benchmarking of GSCM practices to create the initial improvement guidelines. Consequently, the application of ISM creates a roadmap for the improvement of each practice through the categorization by driver, linkage, and dependent practices. ISM also allows for additional interpretations through the computation of the driving and dependence power for each practice and the identification of the corresponding level for each set of practices. In sum, the proposed SMT creates the potential for further improvements through the integration of the RRI scores and ISM results. Hence, the proposed hybrid model offers a practical tool for decision-makers.