# Developing a New Interval Type-2 Hesitant Fuzzy TOPSIS-Based Fuzzy Best-Worst Multicriteria Decision-Making Method for Competitive Pricing in Supply Chain

Authors: Farhad Salimian; Maryam Damiri; Mahyar Ramezankhani; Saeed Khakshouri Fariman
Journal: Journal of Mathematics
 (2022)

---

## Abstract

Corporate social responsibility based on competitive supply chain pricing has been considered the most efficient strategy of a business globally. In an MCDM challenge, a competent supply chain pricing considerations solutions supplier considers multiple possibly competing for qualitative and quantitative criteria. This study presented a new extended decision-making and FBWM method based on type-2 fuzzy sets TOPSIS for ranking the corporate social responsibility criteria based on competitive supply chain pricing factors. The aim of the ranking is to learn how a company and its supply chain may accomplish joint-optimization of economic, environmental, and social performance by transparently and strategically integrating sustainability into business operations. To verify the efficacy of the suggested strategy, it is compared to a benchmarking model. IT2F VIKOR is the subject of this benchmarking model. The significance weights of the competitive supply chain pricing selection criterion are determined from BWM for both techniques. The results show that the most probable dimension of social responsibility in knowledge-based businesses in the oil sector, according to the research, is the environmental dimension in social responsibility, which may produce competitive supply chain pricing mechanisms based on profitability. Other findings revealed that the elements of competitive supply chain pricing factors in oil companies regarding social responsibility are ranked as follows: customer, product, and competitive supply chain pricing procedures. As a result, maintaining customer values is a major price element in establishing contacts, based on the company’s strategic connection with the economy and regional and worldwide partners.


---

## Body

## 1. Introduction

Corporate sustainability has become a term in today’s corporate world. According to statistics, 93 percent of world CEOs feel that maintaining a competitive standard without embracing sustainability is complex [1]. The World Commission on Environment and Development defines corporate sustainability as “meeting the requirements of the existing generation without jeopardizing future generations’ ability to satisfy their needs,” and this concept demonstrates how businesses are vulnerable to long-term sustainability if they do not care about the environment [2]. In today’s modern world, all firms, particularly oil and gas companies, must have an efficient, sustainable plan since these companies frequently fail to meet the triple bottom of a sustainable plan [3]. The triple bottom line (TBL) of sustainable plans is the total assessment of economic, social, and environmental considerations. The corporate context of this TBL is so critical that today’s oil and gas firms cannot operate without one [4]. This TBL notion refers to a situation in which businesses combine their economic objectives with the acceptance of responsibility for their influence on ecosystems and people [5]. Because of the notion that corporate sustainability has become the heart of company success, companies are trying to deploy cutting-edge sustainability strategies [6]. Competitive functions founded on political, social, cultural, and economic development will grow more complicated as the condition of expectations and societal approaches transformation, and this is one of the key challenges confronting businesses, particularly knowledge-based firms [7]. The important aspect here is that an institution or company will be able to engage in and assist stakeholders. It is considered to be a social duty in the context of competitive procedures to maintain the overwhelming interests [8, 9]. Other conventional management supply chain management methodologies that pursued less integration in their processes, on the other hand, do not have the necessary efficacy in today’s competitive world since the environmental change, social development, technological development, and increasing cultural contradictions have all decided to bring about comprehensive alterations in this region [10]. The necessity to make the correct decision and select the most suitable among many possibilities in diverse areas, including choosing the perfect manufacturer, distributor, best area to draw more customers, best business alliance in shaping integrations, most efficient pricing process, and so on, are critical issues for making decisions in supply chain management [11, 12]. These judgments cover a wide range of topics, from trivial concerns to serious difficulties. Worries about the influence of business on the environment and society have prompted further study into sustainable supply chain management (SSCM) [13]. This area of study aims to learn how a company and its supply chain may accomplish joint-optimization of economic, environmental, and social performance by integrating sustainability into business operations transparently and strategically. In an MCDM challenge, a competent supply chain pricing consideration solutions supplier considers multiple potentially competing qualitative and quantitative criteria. This study developed an extended decision-making and fuzzy set TOPSIS model based on competitive supply chain pricing factors to rank the corporate social responsibility criteria. By transparently and strategically integrating sustainability into business operations, companies and their supply chains can optimize economic, environmental, and social performance. As a result, the goal of this study is to look at the most efficient operation of competitive supply chain pricing focused on social responsibility dimensions with a case study of oil products from knowledge-based enterprises.


## 2. Literature Review

### 2.1. Supply Chain Performance Analysis

Sustainable supply chain management is the systematic integration of essential business operations that allows businesses and their supply networks to achieve economic, environmental, and social objectives [14]. As a result, firms who apply it can meet short-term financial objectives while also proactively dealing with supply chain activities that have long-term environmental and social implications. Since its products are broadly employed in economic and social activities, the oil sector is critical for long-term sustainability. The use of sustainable supply chain management may help to mitigate the harmful effects of operations. Even though sustainable supply chain management as a discipline has matured, sustainable supply chain management research in the oil sector is still in its infancy [15]. The focus of previous studies on the topic has been on increasing supply chain performance through innovative techniques and open innovation efforts [16–18]. The research also looked at how suppliers may improve by using management frameworks to detect and manage environmental and social risks in their operations [19]. It is also working on a green supply chain management method to assess environmental sustainability in oil refinery operations and assess the environmental impacts of decommissioning offshore oil and gas operational systems [20]. Sharifi et al. examined the influence of artificial intelligence and the digital era on business and energy in the aftermath of the COVID-19 pandemic [21]. Finally, the research focused on the problems of integrating corporate strategy topics in supply chain management and close collaboration between oil companies and suppliers to improve the environmental performance of logistical operations [22]. Many scholars are currently discussing the impact of corporate social responsibility on closed-loop supply chain operations. Panda et al. [23] used consumer surplus as an indicator to measure the degree of corporate social responsibility fulfillment by companies, and they concluded that a company’s corporate social responsibility behavior might help improve the overall performance of the corporate social responsibility system. When customers pay differentially for new and remanufactured products [23], Dai et al. [24] studied the impact of corporate social responsibility behavior on price decisions and supply chain coordination in the remanufacturing supply chain. Liu et al. [25] examined the influence of corporate social responsibility choices and the number of green consumers on selecting reverse recycling routes, pricing, and recycling ratios in CLSC. Wang et al. [26] investigated how state subsidies and corporate product contributions influenced closed-loop supply chain recycling and pricing decisions.


### 2.2. Insight to Social Responsibility

Modak et al. [27] developed four recycling decision models for the closed-loop supply chain based on the manufacturer’s corporate social responsibility inputs and looked into the influence of the manufacturer’s corporate social responsibility input behavior on the closed-loop supply chain choice of the best recycling channel. Past studies on supply chain coordination focused mainly on contract coordination from economic returns or profit maximization [27]. This has evolved as customers have become more aware of corporate social responsibility [24, 25]. The number of studies on long-term supply chains and supply chain coordination that consider economic and environmental advantages has increased [26, 27]. Corporate social responsibility has a major impact on supply chain operations, with research demonstrating that as the number of corporate social responsibility -sensitive consumers grows, so does the manufacturer’s corporate social responsibility level, as well as the manufacturer’s and retailer’s profitability [25]. Johari and Hosseini-Motlagh [28] found that the recommended corporate social responsibility cost-sharing contract enhances market demand by considerably decreasing selling prices and increasing the degree of corporate social responsibility activities in a competitive context. With the growth in consumer awareness of corporate social responsibility, Wang et al. saw corporate social responsibility investment level competition as a critical component of business competitiveness [29]. They discovered that the manufacturer’s profit is convex concerning customers’ environmental protection [30]. A socially conscious company might charge a greater price for its goods [31]. Furthermore, corporate social responsibility implications on supply chain operation and coordination exist in various competitive settings and decision-making structures. There are different corporate social responsibility implications in different channel configurations, such as exclusive vs. competitive retailing. Ahmadi et al. discussed how to use object-oriented properties programming to create expert systems for strategic planning [32]. Niu et al. [33] observed that an international vaccine provider would prefer competitive retailing when the global and local vaccines are highly interchangeable. It does not matter if the aim is profit or social responsibility. The supply chain will have various social responsibility techniques in monopoly and duopoly situations. When the conditions for each company’s optimal corporate social responsibility efforts, strategic planning preferences, and corporate social responsibility effects in monopoly and duopoly circumstances are considered, Shi et al. [34] discovered that, in the duopoly scenario, the supply chain is more highly likely to be irresponsible and lesser the total corporate social responsibility attempt level. When corporate social responsibility is included, societal welfare in a centralized decision-making scenario is always higher than decentralized [35].A coordination contract to encourage a company to adopt corporate social responsibility, according to Li et al. [36], can lead to multiple wins for social welfare, customers, and supply chain players. However, once the corporate social responsibility coefficient exceeds a specific level, the disagreement between supply chain participants becomes irreconcilable. As a result, the impact of corporate social responsibility on supply chain operations is a complex issue. The complexity of the impact of social responsibility on supply chain coordination has been a source of worry for educators. Using game theory, scholars investigated the influence of negative social responsibility on the design of supply chain coordination contracts. They observed that in a retailer-led supply chain, a cost-sharing arrangement might increase profit for both parties. In a manufacturer-led collaboration, a profit-sharing model can lead to greater overall returns [37]. From the perspective of the global supply chain, Tian et al. [38] observed that increasing the degree of social responsibility of Chinese suppliers might enhance production demand and the economic advantages of stakeholders. On the other hand, supplier earnings will decline as product greenness increases, but stakeholder profits will increase. Nonetheless, Chinese suppliers’ economic earnings are influenced by their level of social responsibility and the advantages of green production. There is presently a serious lack of collaboration in establishing multinational firms’ social responsibility, according to Liu et al. [39]. They created a punishment model that handled supply chain participants’ optimal decision-making in centralized and decentralized decision-making procedures. They then looked at the impact of the penalty % on social responsibility input.


### 2.3. CSR on the Sustainable Supply Chains and Fuzzy TOPSIS Methods

The researchers investigated the construction of resilient supply chains using renewable energy in Lotfi et al. It is suggested that the upper limit for the primary model be less than 10%, which is reasonable for cost estimation of large-scale problems. According to this study, SC should be equipped with RE in order to make them more robust to fluctuations in demand and to serve as an energy resource compatible with the seventh sustainable development agenda (SGD7) [40]. For designing unpredictable mixed resilient-sustainable power logistics, Motlagh et al. developed an innovative technique. In terms of standard deviation and mean value of the overall cost, the results demonstrate that decision-makers can distribute uncertainty budget and safety level efficiently to get more robust solutions using fuzzy-robust techniques [41]. Ahmadi examined how knowledge-based indicators affect economic development and classified and ranked them using logarithmic fuzzy preference programming [42]. Lahri et al. designed an innovative integrated BWM Sustainability supply chain network. This study used the Epsilon-constraint approach coupled with possibility programming, which has been documented sparingly in the literature. Multiple Pareto-optimal alternatives can be found by applying the Epsilon-constraint, allowing for a broad range of trade-offs across cost, emission, and ecological equity. When facing an unstable world, the findings help decision-makers [43]. As part of a combination stochastic fuzzy-robust methodology, Sharifi et al. investigated unique resilient-sustainable techniques for the construction of third biofuel networks from neem and eruca sativa. Using three factors, including resilience, social consequences, and cost, and weighing their relative importance, decision-makers can optimize biofuel logistics network architecture. In order to build a healthy supply chain system, Eskandarpour et al. conducted a multi-directional search. In conclusion, the article discusses the influence of greener technologies on supply chain structure from a management perspective [44].


## 2.1. Supply Chain Performance Analysis

Sustainable supply chain management is the systematic integration of essential business operations that allows businesses and their supply networks to achieve economic, environmental, and social objectives [14]. As a result, firms who apply it can meet short-term financial objectives while also proactively dealing with supply chain activities that have long-term environmental and social implications. Since its products are broadly employed in economic and social activities, the oil sector is critical for long-term sustainability. The use of sustainable supply chain management may help to mitigate the harmful effects of operations. Even though sustainable supply chain management as a discipline has matured, sustainable supply chain management research in the oil sector is still in its infancy [15]. The focus of previous studies on the topic has been on increasing supply chain performance through innovative techniques and open innovation efforts [16–18]. The research also looked at how suppliers may improve by using management frameworks to detect and manage environmental and social risks in their operations [19]. It is also working on a green supply chain management method to assess environmental sustainability in oil refinery operations and assess the environmental impacts of decommissioning offshore oil and gas operational systems [20]. Sharifi et al. examined the influence of artificial intelligence and the digital era on business and energy in the aftermath of the COVID-19 pandemic [21]. Finally, the research focused on the problems of integrating corporate strategy topics in supply chain management and close collaboration between oil companies and suppliers to improve the environmental performance of logistical operations [22]. Many scholars are currently discussing the impact of corporate social responsibility on closed-loop supply chain operations. Panda et al. [23] used consumer surplus as an indicator to measure the degree of corporate social responsibility fulfillment by companies, and they concluded that a company’s corporate social responsibility behavior might help improve the overall performance of the corporate social responsibility system. When customers pay differentially for new and remanufactured products [23], Dai et al. [24] studied the impact of corporate social responsibility behavior on price decisions and supply chain coordination in the remanufacturing supply chain. Liu et al. [25] examined the influence of corporate social responsibility choices and the number of green consumers on selecting reverse recycling routes, pricing, and recycling ratios in CLSC. Wang et al. [26] investigated how state subsidies and corporate product contributions influenced closed-loop supply chain recycling and pricing decisions.


## 2.2. Insight to Social Responsibility

Modak et al. [27] developed four recycling decision models for the closed-loop supply chain based on the manufacturer’s corporate social responsibility inputs and looked into the influence of the manufacturer’s corporate social responsibility input behavior on the closed-loop supply chain choice of the best recycling channel. Past studies on supply chain coordination focused mainly on contract coordination from economic returns or profit maximization [27]. This has evolved as customers have become more aware of corporate social responsibility [24, 25]. The number of studies on long-term supply chains and supply chain coordination that consider economic and environmental advantages has increased [26, 27]. Corporate social responsibility has a major impact on supply chain operations, with research demonstrating that as the number of corporate social responsibility -sensitive consumers grows, so does the manufacturer’s corporate social responsibility level, as well as the manufacturer’s and retailer’s profitability [25]. Johari and Hosseini-Motlagh [28] found that the recommended corporate social responsibility cost-sharing contract enhances market demand by considerably decreasing selling prices and increasing the degree of corporate social responsibility activities in a competitive context. With the growth in consumer awareness of corporate social responsibility, Wang et al. saw corporate social responsibility investment level competition as a critical component of business competitiveness [29]. They discovered that the manufacturer’s profit is convex concerning customers’ environmental protection [30]. A socially conscious company might charge a greater price for its goods [31]. Furthermore, corporate social responsibility implications on supply chain operation and coordination exist in various competitive settings and decision-making structures. There are different corporate social responsibility implications in different channel configurations, such as exclusive vs. competitive retailing. Ahmadi et al. discussed how to use object-oriented properties programming to create expert systems for strategic planning [32]. Niu et al. [33] observed that an international vaccine provider would prefer competitive retailing when the global and local vaccines are highly interchangeable. It does not matter if the aim is profit or social responsibility. The supply chain will have various social responsibility techniques in monopoly and duopoly situations. When the conditions for each company’s optimal corporate social responsibility efforts, strategic planning preferences, and corporate social responsibility effects in monopoly and duopoly circumstances are considered, Shi et al. [34] discovered that, in the duopoly scenario, the supply chain is more highly likely to be irresponsible and lesser the total corporate social responsibility attempt level. When corporate social responsibility is included, societal welfare in a centralized decision-making scenario is always higher than decentralized [35].A coordination contract to encourage a company to adopt corporate social responsibility, according to Li et al. [36], can lead to multiple wins for social welfare, customers, and supply chain players. However, once the corporate social responsibility coefficient exceeds a specific level, the disagreement between supply chain participants becomes irreconcilable. As a result, the impact of corporate social responsibility on supply chain operations is a complex issue. The complexity of the impact of social responsibility on supply chain coordination has been a source of worry for educators. Using game theory, scholars investigated the influence of negative social responsibility on the design of supply chain coordination contracts. They observed that in a retailer-led supply chain, a cost-sharing arrangement might increase profit for both parties. In a manufacturer-led collaboration, a profit-sharing model can lead to greater overall returns [37]. From the perspective of the global supply chain, Tian et al. [38] observed that increasing the degree of social responsibility of Chinese suppliers might enhance production demand and the economic advantages of stakeholders. On the other hand, supplier earnings will decline as product greenness increases, but stakeholder profits will increase. Nonetheless, Chinese suppliers’ economic earnings are influenced by their level of social responsibility and the advantages of green production. There is presently a serious lack of collaboration in establishing multinational firms’ social responsibility, according to Liu et al. [39]. They created a punishment model that handled supply chain participants’ optimal decision-making in centralized and decentralized decision-making procedures. They then looked at the impact of the penalty % on social responsibility input.


## 2.3. CSR on the Sustainable Supply Chains and Fuzzy TOPSIS Methods

The researchers investigated the construction of resilient supply chains using renewable energy in Lotfi et al. It is suggested that the upper limit for the primary model be less than 10%, which is reasonable for cost estimation of large-scale problems. According to this study, SC should be equipped with RE in order to make them more robust to fluctuations in demand and to serve as an energy resource compatible with the seventh sustainable development agenda (SGD7) [40]. For designing unpredictable mixed resilient-sustainable power logistics, Motlagh et al. developed an innovative technique. In terms of standard deviation and mean value of the overall cost, the results demonstrate that decision-makers can distribute uncertainty budget and safety level efficiently to get more robust solutions using fuzzy-robust techniques [41]. Ahmadi examined how knowledge-based indicators affect economic development and classified and ranked them using logarithmic fuzzy preference programming [42]. Lahri et al. designed an innovative integrated BWM Sustainability supply chain network. This study used the Epsilon-constraint approach coupled with possibility programming, which has been documented sparingly in the literature. Multiple Pareto-optimal alternatives can be found by applying the Epsilon-constraint, allowing for a broad range of trade-offs across cost, emission, and ecological equity. When facing an unstable world, the findings help decision-makers [43]. As part of a combination stochastic fuzzy-robust methodology, Sharifi et al. investigated unique resilient-sustainable techniques for the construction of third biofuel networks from neem and eruca sativa. Using three factors, including resilience, social consequences, and cost, and weighing their relative importance, decision-makers can optimize biofuel logistics network architecture. In order to build a healthy supply chain system, Eskandarpour et al. conducted a multi-directional search. In conclusion, the article discusses the influence of greener technologies on supply chain structure from a management perspective [44].


## 3. Research Methodology

### 3.1. Social Responsibility

Business executives have voiced a diverse range of views on corporate social responsibility (CSR). In his paper, The Social Responsibility of Business is to Raise Its Earnings, Nobel Prize-winning economist Milton Friedman (1970) clearly stated one perspective on corporate social responsibility [45]. ‘‘The business structure that Whole Foods has embraced might symbolize a new kind of capitalism, one that more deliberately works for the greater good instead of relying only on the “invisible hand” to create beneficial benefits for society, said John Mackey, the founder of Whole Foods’’ [46]. Regardless of how fundamentally business executives believe about corporate social responsibility, they cannot disregard corporate social responsibility consequences for their organizations. As a result, the purpose of this article is to offer some assistance to businesses looking to evaluate the advantages and costs of corporate social responsibility. These advantages and expenses can help managers judge their firms’ corporate social responsibility policies and offer feedback on corporate social responsibility initiatives. We concentrate on the relationship between accounting and corporate social responsibility since accounting is a critical assessment tool in businesses.


### 3.2. Corporate Social Responsibility in Oil and Gas

A short assessment of corporate social responsibility in the oil and gas industry immediately connects rising civic expectations and worries about global warming, environmental disasters, dwindling resources, and the impact of transnational corporations on globalization, and perceived expanding power and influence [47]. Consumers and civic organizations increasingly target TNCs to initiate changes by threatening reputational consequences, as skepticism about national governments’ desire and capacity to effect change grows. Larger, more focused use of corporate social responsibility techniques, concepts, and ways of reasoning may allow for the detection of broader future dangers and possibilities [48]. Profits, demand, accessibility, market price, and reputation are all at risk. Energy businesses are especially susceptible in this sense. Intangibles such as trust, dependability, validity, continuity, connections, and clarity, as well as tangibles like profitability, investment in staff, communities, and the environment, are critical to their brand and social capital [49]. Organizations confront rising demands and expectations from governments, communities, and consumers as competition for new areas heats up, which are always linked with wider and deeper risks, markets, and energized civil societies. In the oil and gas business, corporate social responsibility (or other similar words such as CR, sustainable development, communities, and our people) has received increased consideration due to altering civic, political, and commercial realities [50]. Transnational initiatives such as the Global Reporting Initiative, the UN Global Compact, and the UK-led Extractive Industries Transparency Initiative (EITI), as well as organizations like the World Business Council for Sustainable Development, the Institute of Business Ethics, and the World Bank Group-funded CommDev, have been introduced in place of global attempts to control TNCs. Transformation in business behavior is encouraged by such organizations. TNCs have adjusted to shifting attitudes and needs by launching a slew of corporate social responsibility-related activities. Several more oil and gas companies now have codes of ethics, participate in stakeholder dialogue, assist in the construction of schools and hospitals, collaborate with development organizations such as the United Nations Development Programme (UNDP), use nongovernmental organizations (NGOs) to apply for development programs, and publish financial, social, and environmental performance findings. Various worldwide representative organizations, such as IPIECA and the World Petroleum Council, encourage awareness of ethical and sustainable corporate practices and campaigns highlighting firm community involvement [51].Despite this, there is broad recognition within the energy industry and representative organizations and increased calls from international pressure groups, civilian populations, and, in some instances, political parties and militias that a more significant focus on social responsibility is necessary. Employees will need more knowledge into how firms engage with communities, governments, and global processes and the repercussions if enterprises are to constructively contribute to more involvement and the adoption of corporate social responsibility -related ways of thinking. Furthermore, despite the more significant expenditure, corporate social responsibility initiatives fail to address many of the issues they are blamed for causing. Corporate social responsibility rules, on the other hand, exacerbate current issues while creating new ones. The issues are exacerbated in regions where oil and gas corporations operate under the so-called absent state theory [52].


### 3.3. Sustainable Supply Chain Management

The sustainable supply chain management approach was examined on two levels: first, as a single idea, regardless of the numerous particular tactics used by oil distribution businesses to promote sustainable supply chain management; and second, as a collection of methods used by oil distribution firms to promote sustainable supply chain management. Second, as particular strategies—green supplier selection, product stewardship, and logistics management [53]. In the administration of a supply chain, oil and gas distribution firms face various economic, environmental, and social issues [54, 55]. As a result, SSCM in the oil sector must deal with many challenges, including pollution impact replacement, elevated energy, material use, and biodiversity conservation [56]. Rather than dealing with each possible issue individually, oil firms opt to follow conventions or use well-established criteria [57]. Codes of conduct, norms, and standards, as well as certification that validates compliance, can be used by oil and gas distribution companies to assess a supplier’s environmental or social impact [57, 58]. All of these are elements that can help you improve the efficiency of your supply chain. Since oil and gas distribution companies control their supply chains, they evaluate vendors by establishing minimum requirements and standards. Consequently, suppliers may conduct self-evaluations based on environmental or social criteria, which may be helped by incentives [59].


#### 3.3.1. Product Stewardship Strategy

For example, several facets of sustainability are included in the Product Stewardship approach, reverse logistics, and closed-loop supply chains [60]. Lane and Watson (2012) [61] feel that product stewardship is a swiftly expanding area that is highly relevant in resource conservation and producing a competitive advantage for an organization [36]. According to several scholars, environmental stewardship is becoming a more widely used technique in relation to material responsibility [62]. From responsible waste management to a stronger focus on resource conservation, the concept of product stewardship has evolved. The interplay between the social and material worlds has changed due to product stewardship methods [63]. Moreover, these activities have contributed to the development of new industrial sectors and collaborations. The main objective of Product Stewardship is to reduce a product’s environmental effect throughout its life cycle [64].


### 3.4. Supply Chain Management Functions

There are a variety of supply chain management functionalities available, each stressing a distinct perspective [65]. Florescu et al. [58] believe that planning and execution are essential to supply chain activities. However, other researchers [66, 67] suggest that sustainable supply chain management requires a broader emphasis on coordination and cooperation. As a result, four supply chain management functions are investigated in this work: planning, execution, coordination, and cooperation. Creating an sustainable supply chain management necessitates a supply chain focus, active participation of supply chain actors, and teamwork. Waste reduction, material recycling, energy conservation [68], and the development of new environmental development techniques [68, 69] are all examples of new environmental methodological approaches.Moreover, environmental effect data on acquired commodities have been proven to benefit supply chains with adequate planning and coordination. Poor supply chain communication, on the other hand, results in the inadequate transfer of information and has an impact on goal alignment, which is essential to implement environmental sustainability in supply chains [70]. Cooperation among supply chain players affects supply chain sustainability by affecting information transmission and problem-solving among supply chain members [71]. In supply chains, for instance, codes of conduct are employed to establish principles and require communication to reinforce those standards across the network [72]. As a consequence, communication may have a direct influence on the social sustainability performance of the supply chain.


### 3.5. Integration of Supply Chain Pricing Values

The degree to which a manufacturer collaborates with supply chain partners and as a group handles procedures both inside and outside the business to create competitive advantages is known as supply chain pricing integration [73]. Firms have realized that they must handle the supply chain as a whole system and consider it an integrated function as the necessity of good supply chain management has grown [74]. Stevens (1989) initially examined value integration in terms of the three dimensions listed as follows (see Figure 1):Figure 1
A three-dimensional approach to supply chain value integration.When a company adopts an integrated strategy, it recognizes that it cannot handle each component of the supply chain as a separate function. However, it must instead comprehend and manage the linkages and interdependencies. This means that sourcing, demand planning, inventory planning, warehousing, logistics, and order processing must all work simultaneously inside the supply chain organization. The rise of supply chain software solutions that put all of this data together in one place is one indicator of a more integrated approach [75].Furthermore, many businesses traditionally had these tasks dispersed across many departments with limited chance for interaction. Most big corporations now have an integrated supply chain function with a single management team and shared goals. There are crucial links between marketing, finance, and production and the work done inside the supply chain organization. Marketing has a direct role in forecasting demand and setting consumer expectations for goods and delivery. These should be discussed with the supply chain team so that everyone understands what has to be accomplished. If this is not feasible, modifications may be made and communicated to consumers as soon as possible. Integration with finance is required to ensure that investments are adequately planned, and that inventory is correctly accounted for. Manufacturing is frequently influenced by the supply chain team’s decisions and requirements since they rely on enough supplies and must fulfill delivery deadlines to satisfy client promises. This integration functions well when real goals are defined across the companies, a shared perspective of the data (which identifies possibilities for enhanced performance), and clear, frequent communication about possible difficulties and requirements, as with many complicated organizational problems. As a result, all businesses can concentrate on providing value to consumers and fulfilling the company’s goal. For instance, Lee and Hang propose the following model (see Figure2) for supply chain value integration [76]:Figure 2
Supply chain value integration.


### 3.6. Data Statement

In the qualitative portion, the statistical population comprises 30 university-level professionals and specialists in industrial management who have a skilled and scientific approach to performing scientific research in a related subject. They were chosen using a homogenous sample approach since the participants in this section were expected to understand the study issue theoretically. In addition, research accessible on Iranian websites was utilized to identify components (functioning of competitive supply chain pricing) and indicators (social responsibility) of the research in this part, based on the meta-synthesis. The interpretative ranking process (IRP) was carried out in the second phase, with 40 managers from knowledge-based firms in the oil industry filling in the matrix checklists depending on the nature of the study.


### 3.7. Best-Worst Method

For the selection step, the best-worst method (BWM) was developed [30]. The BWM has several distinguishing characteristics, including (i) a highly structured pairwise comparison that yields very continuous and trustworthy findings; (ii) the use of only two vectors rather than a full pairwise comparison matrix. This means that the analyst and decision-maker will have to spend less time gathering data. Surprisingly, because these two vectors are more organized than a whole matrix, less data contributes to improved dependability; (iii) the technique only utilizes integer values, making it more pragmatic than methods requiring fractions.Step 1: create a list of criteria for making a choice. We examine the criteria (c_1, c_2, …, c_n) that should be utilized to choose this phase.Step 2: decide which criteria are the best (most pleasant, most essential) and the worst (least attractive, least essential). In this stage, the decision-maker determines which criteria are the greatest and worst in general. At this point, no comparisons are drawn.Step 3: utilizing a number between 1 and 9, identify the best criterion’s priority above all other criteria. The Best-to-Others vector as a consequence would be:(1)AB=aB1,aB2,…,aBn,whereaBj denotes the best criteria B’s priority over criterion j. aBB=1 is self-evident.Step 4: utilizing a number between 1 and 9, rank all criteria in order of priority over the worst criterion. The Others-to-Worst vector as a consequence would be:(2)AB=a1W,a2W,…,anWT,whereajW denotes the criteria j’s preference over the worst criterion W. aBB=1 is self-evident.Step 5: determine the best weightsw1∗,w2∗,…,wn∗.The weight for the criteria that haswB/wj=ajw for each pair of wB/wj and wj/ww is the best. We should develop a solution that minimizes the most remarkable absolute differences wB/wj−aBj and wj/wW−ajW for every j to meet these. The next difficulty arises from the nonnegativity and sum condition for the weights:(3)minmaxjwBwj−aBj,wjwW−ajW,(4)st∑wj=1,wj≥0.The following problem can be applied to this one:ξ(5)minξwBwj−aBj≤ξfor alljwjwW−ajW≤ξfor allj∑​wj=1wj≥0for allj.The optimal weightsw1∗,w2∗,…,wn∗ and ξ∗ are computed while solving the problem. The consistency index is then used to determine the consistency ratio (Tables 1 and 2). The larger the ξ∗, the greater the consistency ratio, and the less trustworthy the comparisons get.Table 1
Consistency index.
aBW123456789Consistency index0.000.441.001.632.303.003.734.475.23Table 2
For the evaluation of criteria, linguistic variables and TFNs are used.
Linguistic variablesTriangular fuzzy numbers (TFNs)Equally preferred (EP)(1, 1, 1)Moderately preferred (MP)(2/3, 1, 3/2)Strongly preferred (SP)(3/2, 2, 5/2)Very strongly preferred (VSP)(5/2, 3, 7/2)Extremely more preferred (EMP)(7/2, 4, 9/2)First, FBWM [35] is used to calculate the fuzzy criterion weights in the proposed Integrated Fuzzy Best-Worst-TOPSIS approach. In FBWM, DMs indicate their preferences for criterion comparison using the linguistic characteristics listed in Table 1, then translated into TFNs.The FBWM’s stages are listed below.Define the Evaluation Criteria and DMs in Step 1. A set ofC = {c1,c2,…,cm},(benefit) criteria is established, with the greatest being better for each criterion.Step 2: determine which criteria are the best and worst. Each DM determines which criterion is the best (most desired) and which is the worst (least desirable) (less desirable).Step 3: calculate the best criterion’s fuzzy preference over all criteria (Best-to-Others (BO) vector). Utilizing the linguistic words in Table1, each DM decides his or her best criterion’s preference over all other criteria, which is then translated to TFNs a˜˜ijj=1,2,…,n. It is worth noting that a˜˜BB=1,1,1.Step 4: Over the Worst criterion (OW) vector, calculate the fuzzy preference of all other criteria. Utilizing the linguistic words in Table1, each DM calculates the priority of all other criteria over the worst criterion, which is then translated to TFNs a˜˜jwj=1,2,…,n. It is worth noting that a˜˜BB=1,1,1.Step 5: determine the criteria’s optimal fuzzy weights. Problem (7) is addressed for each DM and the ideal fuzzy weight vector w−=w1−,w2−,…,wm− and ξ∗ are found.Step 6: make sure everything is consistent.There is a sum of2n − 3 fuzzy reference comparisons (n − 2 best-to-others fuzzy comparisons + n − 2 others-to-worst fuzzy comparisons +1 best-to-worst fuzzy comparison), which is required to be applied for FBWM. It’s worth noting that (fuzzy) BWM only requires 2n − 3 comparisons, but (fuzzy) AHP requires (n − 1)/2.In FBWM, optimal fuzzy weightsw−=Ij,mj,ujj = 1, 2, …, n can be determined as follows [6, 31]:(6)minmaxjw˜Bw˜j−a˜Bj,w˜jw˜W−a˜jW,s.t.∑j=1nRw˜j=1,lj≤mj≤uj,j=1,2,…,n,lj≥0,j=1,2,…,n,where w˜B=lB,mB,uBw˜W=lW,mW,uWa˜Bj=lBjmBj,uBja˜jW=ljW2mjw,ujW The following nonlinearly constrained optimization problem may be converted to problem (3):(7)s.t.minξ˜∣w˜Bw˜j−a˜Bj≤ξ˜,j=1,2,…,nw˜jw˜W−a˜jW∣≤ξ˜,j=1,2,…,n∑j=1nRw˜j=1,lj≤mj≤uj,j=1,2,…,n,lj≥0,j=1,2,…,n,where ξ˜=lξ,mξ,uξ taking into account lξ<mξ<uξ presume ξ∗ = (k∗, k∗, k∗), K∗ ≤ lξ then problem (6) can be converted to the following problem:(8)minξ˜s.t.lB,mB,uBlj,mj,uj−lBj,mBj,uBj≤k∗,k∗,k∗j=1,2,…,nlj,mj,ujlW,mW,uW−ljWmjw,ujw|≤k∗,k∗,k∗j=1,2,…,n∑j=1nRw˜j=1lj≤mj≤uj,j=1,2,…,nlj≥0,j=1,2,…,n.The optimal fuzzy weightsw˜j=Ij,mj,ujj=1,2…,nandξ˜∗ may be found by solving the preceding issue.Testing for consistency [34]: to evaluate the consistency of fuzzy pairwise comparisons, the consistency ratio (CR) is utilized.Whena˜Bj.a˜Bj=a˜BW, a fuzzy comparison is entirely consistent.The most excellent possible crisp may be calculated and utilized as a consistency index (CI) for FBWM by calculating equation (8) for various values a˜BW=lBW,mBWuBW:(9)ξ2−1+2uBWξ+uBW2−uBW=0.Table2 shows the estimated CI values for various variables.After that, the consistency ratio (CR) is determined as follows: Closer to zero CR scores indicate great consistency.


### 3.8. Interval Type-2 Fuzzy Sets TOPSIS (IT2F-TOPSIS)

The TOPSIS approach aims to choose the closest alternatives to the positive-ideal solution and furthest away from the negative-ideal solution [77, 78]. IT2F-TOPSIS Pythagorean fuzzy variants of TOPSIS based on fuzzy collections are created as Ak and Gul [74]. Utilizing the benefits of IT2FSs, TOPSIS centered on IT2FSs better represents uncertainty, vagueness, and ambiguity than regular fuzzy TOPSIS [78]. The suggested TOPSIS, which is based on IT2FSs, may be implemented in several ways:Step 1: suppose that there are S options, withS=s1,s2,…,sn Furthermore, that there are C criteria, with C = {c1,c2,…, cm}, and that there are K decision-makers. D = {D1, D2,…, DK} Each decision-maker took part in our survey and assigned a value to the supplier’s performance based on each criterion. Equation (1) may be used to compute the supplier’s overall performance value based on each criterion:(10)s1s2⋯snEc=c˜˜ijpm×n=c1c2⋮cmc˜˜11pc˜˜12p⋯c˜˜1npc˜˜21pc˜˜22p⋯c˜˜2np⋮⋮⋮⋮c˜˜m1pc˜˜m2p⋯c˜˜mnp,wherec˜˜ij=c˜˜ij1⊕c˜˜ij2⊕⋯⊕c˜˜ijk/k,s˜˜ij is an IT2FS 1 ≤ i ≤ m,1 ≤ j ≤ n,1c ≤ k, and k show the number of professionals,(11)c˜˜ij=ai1U,ai2U,ai3U,ai4U;H1A˜iU,H2A˜iU,ai1L,ai2L,ai3L,ai4L;H1A˜iL,H2A˜iL,andk show the number of professionals,Step 2: applying the best-worst approach, get the weighting matrix Ws of the criterion,(12)c1c2⋯cmWs=wis1×m=w1s,w2s,⋯,wms.Step 3: to produce the weighted decision matrix, multiply the significant weights of the criterion by the values in the decision matrix. For each criterion, the weighted decision matrixv˜˜ is written as follows:(13)v˜˜ij=c˜˜ij×wi,where the weighted trapezoidal interval type-2 fuzzy integers are denoted byv˜˜ij.Step 4: determine the IT2FSs’ ranking valueRankv˜˜ij. Ew is a ranking weighted decision matrix that is built.Step 5: identify the positive-ideal solutionx∗=v1∗,v2∗,…,vm∗ as well as the negative-ideal solution x¯=v¯1,v¯2,…,vm¯, where(14)vi∗=maxi≤j≤nRankv˜˜ij,iffi∈B,mini≤j≤nRankv˜˜ij,iffi∈C,(15)vi−=mini≤j≤nRankv˜˜ij,iffi∈B,maxi≤j≤nRankv˜˜ij,iffi∈C.Then, for the green supplier, positived∗(xj) and negative d−(xj) ideal solutions are obtained as follows:(16)d∗xj=∑i∈IRankv˜˜ij−vi∗2,(17)d−xj=∑i∈IRankv˜˜ij−vi−2.The trapezoidal IT2FSA˜˜i′s ranking value RankA˜˜i is formulated as follow:(18)Rankv˜˜i=M1v˜iU+M1v˜iL+M2v˜iU+M2v˜iL+M3v˜iU+M3v˜iL−14S1v˜iU+S1v˜iL+S2v˜iU+S2v˜iL+S3v˜iU+S3v˜iL+S4v˜iU+S4v˜iL+H1v˜iU+H1v˜iL+H2v˜iU+H2v˜iL,whereMpv˜˜ij indicates the element’s average of vipj, and vip+1j, denotes the element’s standard deviation of vipj and viqj and viq+1j’s standard deviations are denoted by vip+1j, and Mpv˜˜ij=vipj+vip+1j/2,1≤p≤3,.Sqv˜ij=1/2∑k=qq+1vikj−1/2∑k=qq+1vikj2,1≤q≤3,Hpv˜ij denotes the.In the trapezoidal membership functionv˜ij,1≤p≤3,j∈U,L,and1≤i≤n., the membership value of the element vip+1j.Step 6: the CC(xj) closeness coefficient is then computed.(19)CCxj=d−xjd∗xj+d−xj.Step 7: alternatives can be ranked in descending order. The greater the value of CC(xj), the larger the green supplier’s preference.


## 3.1. Social Responsibility

Business executives have voiced a diverse range of views on corporate social responsibility (CSR). In his paper, The Social Responsibility of Business is to Raise Its Earnings, Nobel Prize-winning economist Milton Friedman (1970) clearly stated one perspective on corporate social responsibility [45]. ‘‘The business structure that Whole Foods has embraced might symbolize a new kind of capitalism, one that more deliberately works for the greater good instead of relying only on the “invisible hand” to create beneficial benefits for society, said John Mackey, the founder of Whole Foods’’ [46]. Regardless of how fundamentally business executives believe about corporate social responsibility, they cannot disregard corporate social responsibility consequences for their organizations. As a result, the purpose of this article is to offer some assistance to businesses looking to evaluate the advantages and costs of corporate social responsibility. These advantages and expenses can help managers judge their firms’ corporate social responsibility policies and offer feedback on corporate social responsibility initiatives. We concentrate on the relationship between accounting and corporate social responsibility since accounting is a critical assessment tool in businesses.


## 3.2. Corporate Social Responsibility in Oil and Gas

A short assessment of corporate social responsibility in the oil and gas industry immediately connects rising civic expectations and worries about global warming, environmental disasters, dwindling resources, and the impact of transnational corporations on globalization, and perceived expanding power and influence [47]. Consumers and civic organizations increasingly target TNCs to initiate changes by threatening reputational consequences, as skepticism about national governments’ desire and capacity to effect change grows. Larger, more focused use of corporate social responsibility techniques, concepts, and ways of reasoning may allow for the detection of broader future dangers and possibilities [48]. Profits, demand, accessibility, market price, and reputation are all at risk. Energy businesses are especially susceptible in this sense. Intangibles such as trust, dependability, validity, continuity, connections, and clarity, as well as tangibles like profitability, investment in staff, communities, and the environment, are critical to their brand and social capital [49]. Organizations confront rising demands and expectations from governments, communities, and consumers as competition for new areas heats up, which are always linked with wider and deeper risks, markets, and energized civil societies. In the oil and gas business, corporate social responsibility (or other similar words such as CR, sustainable development, communities, and our people) has received increased consideration due to altering civic, political, and commercial realities [50]. Transnational initiatives such as the Global Reporting Initiative, the UN Global Compact, and the UK-led Extractive Industries Transparency Initiative (EITI), as well as organizations like the World Business Council for Sustainable Development, the Institute of Business Ethics, and the World Bank Group-funded CommDev, have been introduced in place of global attempts to control TNCs. Transformation in business behavior is encouraged by such organizations. TNCs have adjusted to shifting attitudes and needs by launching a slew of corporate social responsibility-related activities. Several more oil and gas companies now have codes of ethics, participate in stakeholder dialogue, assist in the construction of schools and hospitals, collaborate with development organizations such as the United Nations Development Programme (UNDP), use nongovernmental organizations (NGOs) to apply for development programs, and publish financial, social, and environmental performance findings. Various worldwide representative organizations, such as IPIECA and the World Petroleum Council, encourage awareness of ethical and sustainable corporate practices and campaigns highlighting firm community involvement [51].Despite this, there is broad recognition within the energy industry and representative organizations and increased calls from international pressure groups, civilian populations, and, in some instances, political parties and militias that a more significant focus on social responsibility is necessary. Employees will need more knowledge into how firms engage with communities, governments, and global processes and the repercussions if enterprises are to constructively contribute to more involvement and the adoption of corporate social responsibility -related ways of thinking. Furthermore, despite the more significant expenditure, corporate social responsibility initiatives fail to address many of the issues they are blamed for causing. Corporate social responsibility rules, on the other hand, exacerbate current issues while creating new ones. The issues are exacerbated in regions where oil and gas corporations operate under the so-called absent state theory [52].


## 3.3. Sustainable Supply Chain Management

The sustainable supply chain management approach was examined on two levels: first, as a single idea, regardless of the numerous particular tactics used by oil distribution businesses to promote sustainable supply chain management; and second, as a collection of methods used by oil distribution firms to promote sustainable supply chain management. Second, as particular strategies—green supplier selection, product stewardship, and logistics management [53]. In the administration of a supply chain, oil and gas distribution firms face various economic, environmental, and social issues [54, 55]. As a result, SSCM in the oil sector must deal with many challenges, including pollution impact replacement, elevated energy, material use, and biodiversity conservation [56]. Rather than dealing with each possible issue individually, oil firms opt to follow conventions or use well-established criteria [57]. Codes of conduct, norms, and standards, as well as certification that validates compliance, can be used by oil and gas distribution companies to assess a supplier’s environmental or social impact [57, 58]. All of these are elements that can help you improve the efficiency of your supply chain. Since oil and gas distribution companies control their supply chains, they evaluate vendors by establishing minimum requirements and standards. Consequently, suppliers may conduct self-evaluations based on environmental or social criteria, which may be helped by incentives [59].


### 3.3.1. Product Stewardship Strategy

For example, several facets of sustainability are included in the Product Stewardship approach, reverse logistics, and closed-loop supply chains [60]. Lane and Watson (2012) [61] feel that product stewardship is a swiftly expanding area that is highly relevant in resource conservation and producing a competitive advantage for an organization [36]. According to several scholars, environmental stewardship is becoming a more widely used technique in relation to material responsibility [62]. From responsible waste management to a stronger focus on resource conservation, the concept of product stewardship has evolved. The interplay between the social and material worlds has changed due to product stewardship methods [63]. Moreover, these activities have contributed to the development of new industrial sectors and collaborations. The main objective of Product Stewardship is to reduce a product’s environmental effect throughout its life cycle [64].


## 3.3.1. Product Stewardship Strategy

For example, several facets of sustainability are included in the Product Stewardship approach, reverse logistics, and closed-loop supply chains [60]. Lane and Watson (2012) [61] feel that product stewardship is a swiftly expanding area that is highly relevant in resource conservation and producing a competitive advantage for an organization [36]. According to several scholars, environmental stewardship is becoming a more widely used technique in relation to material responsibility [62]. From responsible waste management to a stronger focus on resource conservation, the concept of product stewardship has evolved. The interplay between the social and material worlds has changed due to product stewardship methods [63]. Moreover, these activities have contributed to the development of new industrial sectors and collaborations. The main objective of Product Stewardship is to reduce a product’s environmental effect throughout its life cycle [64].


## 3.4. Supply Chain Management Functions

There are a variety of supply chain management functionalities available, each stressing a distinct perspective [65]. Florescu et al. [58] believe that planning and execution are essential to supply chain activities. However, other researchers [66, 67] suggest that sustainable supply chain management requires a broader emphasis on coordination and cooperation. As a result, four supply chain management functions are investigated in this work: planning, execution, coordination, and cooperation. Creating an sustainable supply chain management necessitates a supply chain focus, active participation of supply chain actors, and teamwork. Waste reduction, material recycling, energy conservation [68], and the development of new environmental development techniques [68, 69] are all examples of new environmental methodological approaches.Moreover, environmental effect data on acquired commodities have been proven to benefit supply chains with adequate planning and coordination. Poor supply chain communication, on the other hand, results in the inadequate transfer of information and has an impact on goal alignment, which is essential to implement environmental sustainability in supply chains [70]. Cooperation among supply chain players affects supply chain sustainability by affecting information transmission and problem-solving among supply chain members [71]. In supply chains, for instance, codes of conduct are employed to establish principles and require communication to reinforce those standards across the network [72]. As a consequence, communication may have a direct influence on the social sustainability performance of the supply chain.


## 3.5. Integration of Supply Chain Pricing Values

The degree to which a manufacturer collaborates with supply chain partners and as a group handles procedures both inside and outside the business to create competitive advantages is known as supply chain pricing integration [73]. Firms have realized that they must handle the supply chain as a whole system and consider it an integrated function as the necessity of good supply chain management has grown [74]. Stevens (1989) initially examined value integration in terms of the three dimensions listed as follows (see Figure 1):Figure 1
A three-dimensional approach to supply chain value integration.When a company adopts an integrated strategy, it recognizes that it cannot handle each component of the supply chain as a separate function. However, it must instead comprehend and manage the linkages and interdependencies. This means that sourcing, demand planning, inventory planning, warehousing, logistics, and order processing must all work simultaneously inside the supply chain organization. The rise of supply chain software solutions that put all of this data together in one place is one indicator of a more integrated approach [75].Furthermore, many businesses traditionally had these tasks dispersed across many departments with limited chance for interaction. Most big corporations now have an integrated supply chain function with a single management team and shared goals. There are crucial links between marketing, finance, and production and the work done inside the supply chain organization. Marketing has a direct role in forecasting demand and setting consumer expectations for goods and delivery. These should be discussed with the supply chain team so that everyone understands what has to be accomplished. If this is not feasible, modifications may be made and communicated to consumers as soon as possible. Integration with finance is required to ensure that investments are adequately planned, and that inventory is correctly accounted for. Manufacturing is frequently influenced by the supply chain team’s decisions and requirements since they rely on enough supplies and must fulfill delivery deadlines to satisfy client promises. This integration functions well when real goals are defined across the companies, a shared perspective of the data (which identifies possibilities for enhanced performance), and clear, frequent communication about possible difficulties and requirements, as with many complicated organizational problems. As a result, all businesses can concentrate on providing value to consumers and fulfilling the company’s goal. For instance, Lee and Hang propose the following model (see Figure2) for supply chain value integration [76]:Figure 2
Supply chain value integration.


## 3.6. Data Statement

In the qualitative portion, the statistical population comprises 30 university-level professionals and specialists in industrial management who have a skilled and scientific approach to performing scientific research in a related subject. They were chosen using a homogenous sample approach since the participants in this section were expected to understand the study issue theoretically. In addition, research accessible on Iranian websites was utilized to identify components (functioning of competitive supply chain pricing) and indicators (social responsibility) of the research in this part, based on the meta-synthesis. The interpretative ranking process (IRP) was carried out in the second phase, with 40 managers from knowledge-based firms in the oil industry filling in the matrix checklists depending on the nature of the study.


## 3.7. Best-Worst Method

For the selection step, the best-worst method (BWM) was developed [30]. The BWM has several distinguishing characteristics, including (i) a highly structured pairwise comparison that yields very continuous and trustworthy findings; (ii) the use of only two vectors rather than a full pairwise comparison matrix. This means that the analyst and decision-maker will have to spend less time gathering data. Surprisingly, because these two vectors are more organized than a whole matrix, less data contributes to improved dependability; (iii) the technique only utilizes integer values, making it more pragmatic than methods requiring fractions.Step 1: create a list of criteria for making a choice. We examine the criteria (c_1, c_2, …, c_n) that should be utilized to choose this phase.Step 2: decide which criteria are the best (most pleasant, most essential) and the worst (least attractive, least essential). In this stage, the decision-maker determines which criteria are the greatest and worst in general. At this point, no comparisons are drawn.Step 3: utilizing a number between 1 and 9, identify the best criterion’s priority above all other criteria. The Best-to-Others vector as a consequence would be:(1)AB=aB1,aB2,…,aBn,whereaBj denotes the best criteria B’s priority over criterion j. aBB=1 is self-evident.Step 4: utilizing a number between 1 and 9, rank all criteria in order of priority over the worst criterion. The Others-to-Worst vector as a consequence would be:(2)AB=a1W,a2W,…,anWT,whereajW denotes the criteria j’s preference over the worst criterion W. aBB=1 is self-evident.Step 5: determine the best weightsw1∗,w2∗,…,wn∗.The weight for the criteria that haswB/wj=ajw for each pair of wB/wj and wj/ww is the best. We should develop a solution that minimizes the most remarkable absolute differences wB/wj−aBj and wj/wW−ajW for every j to meet these. The next difficulty arises from the nonnegativity and sum condition for the weights:(3)minmaxjwBwj−aBj,wjwW−ajW,(4)st∑wj=1,wj≥0.The following problem can be applied to this one:ξ(5)minξwBwj−aBj≤ξfor alljwjwW−ajW≤ξfor allj∑​wj=1wj≥0for allj.The optimal weightsw1∗,w2∗,…,wn∗ and ξ∗ are computed while solving the problem. The consistency index is then used to determine the consistency ratio (Tables 1 and 2). The larger the ξ∗, the greater the consistency ratio, and the less trustworthy the comparisons get.Table 1
Consistency index.
aBW123456789Consistency index0.000.441.001.632.303.003.734.475.23Table 2
For the evaluation of criteria, linguistic variables and TFNs are used.
Linguistic variablesTriangular fuzzy numbers (TFNs)Equally preferred (EP)(1, 1, 1)Moderately preferred (MP)(2/3, 1, 3/2)Strongly preferred (SP)(3/2, 2, 5/2)Very strongly preferred (VSP)(5/2, 3, 7/2)Extremely more preferred (EMP)(7/2, 4, 9/2)First, FBWM [35] is used to calculate the fuzzy criterion weights in the proposed Integrated Fuzzy Best-Worst-TOPSIS approach. In FBWM, DMs indicate their preferences for criterion comparison using the linguistic characteristics listed in Table 1, then translated into TFNs.The FBWM’s stages are listed below.Define the Evaluation Criteria and DMs in Step 1. A set ofC = {c1,c2,…,cm},(benefit) criteria is established, with the greatest being better for each criterion.Step 2: determine which criteria are the best and worst. Each DM determines which criterion is the best (most desired) and which is the worst (least desirable) (less desirable).Step 3: calculate the best criterion’s fuzzy preference over all criteria (Best-to-Others (BO) vector). Utilizing the linguistic words in Table1, each DM decides his or her best criterion’s preference over all other criteria, which is then translated to TFNs a˜˜ijj=1,2,…,n. It is worth noting that a˜˜BB=1,1,1.Step 4: Over the Worst criterion (OW) vector, calculate the fuzzy preference of all other criteria. Utilizing the linguistic words in Table1, each DM calculates the priority of all other criteria over the worst criterion, which is then translated to TFNs a˜˜jwj=1,2,…,n. It is worth noting that a˜˜BB=1,1,1.Step 5: determine the criteria’s optimal fuzzy weights. Problem (7) is addressed for each DM and the ideal fuzzy weight vector w−=w1−,w2−,…,wm− and ξ∗ are found.Step 6: make sure everything is consistent.There is a sum of2n − 3 fuzzy reference comparisons (n − 2 best-to-others fuzzy comparisons + n − 2 others-to-worst fuzzy comparisons +1 best-to-worst fuzzy comparison), which is required to be applied for FBWM. It’s worth noting that (fuzzy) BWM only requires 2n − 3 comparisons, but (fuzzy) AHP requires (n − 1)/2.In FBWM, optimal fuzzy weightsw−=Ij,mj,ujj = 1, 2, …, n can be determined as follows [6, 31]:(6)minmaxjw˜Bw˜j−a˜Bj,w˜jw˜W−a˜jW,s.t.∑j=1nRw˜j=1,lj≤mj≤uj,j=1,2,…,n,lj≥0,j=1,2,…,n,where w˜B=lB,mB,uBw˜W=lW,mW,uWa˜Bj=lBjmBj,uBja˜jW=ljW2mjw,ujW The following nonlinearly constrained optimization problem may be converted to problem (3):(7)s.t.minξ˜∣w˜Bw˜j−a˜Bj≤ξ˜,j=1,2,…,nw˜jw˜W−a˜jW∣≤ξ˜,j=1,2,…,n∑j=1nRw˜j=1,lj≤mj≤uj,j=1,2,…,n,lj≥0,j=1,2,…,n,where ξ˜=lξ,mξ,uξ taking into account lξ<mξ<uξ presume ξ∗ = (k∗, k∗, k∗), K∗ ≤ lξ then problem (6) can be converted to the following problem:(8)minξ˜s.t.lB,mB,uBlj,mj,uj−lBj,mBj,uBj≤k∗,k∗,k∗j=1,2,…,nlj,mj,ujlW,mW,uW−ljWmjw,ujw|≤k∗,k∗,k∗j=1,2,…,n∑j=1nRw˜j=1lj≤mj≤uj,j=1,2,…,nlj≥0,j=1,2,…,n.The optimal fuzzy weightsw˜j=Ij,mj,ujj=1,2…,nandξ˜∗ may be found by solving the preceding issue.Testing for consistency [34]: to evaluate the consistency of fuzzy pairwise comparisons, the consistency ratio (CR) is utilized.Whena˜Bj.a˜Bj=a˜BW, a fuzzy comparison is entirely consistent.The most excellent possible crisp may be calculated and utilized as a consistency index (CI) for FBWM by calculating equation (8) for various values a˜BW=lBW,mBWuBW:(9)ξ2−1+2uBWξ+uBW2−uBW=0.Table2 shows the estimated CI values for various variables.After that, the consistency ratio (CR) is determined as follows: Closer to zero CR scores indicate great consistency.


## 3.8. Interval Type-2 Fuzzy Sets TOPSIS (IT2F-TOPSIS)

The TOPSIS approach aims to choose the closest alternatives to the positive-ideal solution and furthest away from the negative-ideal solution [77, 78]. IT2F-TOPSIS Pythagorean fuzzy variants of TOPSIS based on fuzzy collections are created as Ak and Gul [74]. Utilizing the benefits of IT2FSs, TOPSIS centered on IT2FSs better represents uncertainty, vagueness, and ambiguity than regular fuzzy TOPSIS [78]. The suggested TOPSIS, which is based on IT2FSs, may be implemented in several ways:Step 1: suppose that there are S options, withS=s1,s2,…,sn Furthermore, that there are C criteria, with C = {c1,c2,…, cm}, and that there are K decision-makers. D = {D1, D2,…, DK} Each decision-maker took part in our survey and assigned a value to the supplier’s performance based on each criterion. Equation (1) may be used to compute the supplier’s overall performance value based on each criterion:(10)s1s2⋯snEc=c˜˜ijpm×n=c1c2⋮cmc˜˜11pc˜˜12p⋯c˜˜1npc˜˜21pc˜˜22p⋯c˜˜2np⋮⋮⋮⋮c˜˜m1pc˜˜m2p⋯c˜˜mnp,wherec˜˜ij=c˜˜ij1⊕c˜˜ij2⊕⋯⊕c˜˜ijk/k,s˜˜ij is an IT2FS 1 ≤ i ≤ m,1 ≤ j ≤ n,1c ≤ k, and k show the number of professionals,(11)c˜˜ij=ai1U,ai2U,ai3U,ai4U;H1A˜iU,H2A˜iU,ai1L,ai2L,ai3L,ai4L;H1A˜iL,H2A˜iL,andk show the number of professionals,Step 2: applying the best-worst approach, get the weighting matrix Ws of the criterion,(12)c1c2⋯cmWs=wis1×m=w1s,w2s,⋯,wms.Step 3: to produce the weighted decision matrix, multiply the significant weights of the criterion by the values in the decision matrix. For each criterion, the weighted decision matrixv˜˜ is written as follows:(13)v˜˜ij=c˜˜ij×wi,where the weighted trapezoidal interval type-2 fuzzy integers are denoted byv˜˜ij.Step 4: determine the IT2FSs’ ranking valueRankv˜˜ij. Ew is a ranking weighted decision matrix that is built.Step 5: identify the positive-ideal solutionx∗=v1∗,v2∗,…,vm∗ as well as the negative-ideal solution x¯=v¯1,v¯2,…,vm¯, where(14)vi∗=maxi≤j≤nRankv˜˜ij,iffi∈B,mini≤j≤nRankv˜˜ij,iffi∈C,(15)vi−=mini≤j≤nRankv˜˜ij,iffi∈B,maxi≤j≤nRankv˜˜ij,iffi∈C.Then, for the green supplier, positived∗(xj) and negative d−(xj) ideal solutions are obtained as follows:(16)d∗xj=∑i∈IRankv˜˜ij−vi∗2,(17)d−xj=∑i∈IRankv˜˜ij−vi−2.The trapezoidal IT2FSA˜˜i′s ranking value RankA˜˜i is formulated as follow:(18)Rankv˜˜i=M1v˜iU+M1v˜iL+M2v˜iU+M2v˜iL+M3v˜iU+M3v˜iL−14S1v˜iU+S1v˜iL+S2v˜iU+S2v˜iL+S3v˜iU+S3v˜iL+S4v˜iU+S4v˜iL+H1v˜iU+H1v˜iL+H2v˜iU+H2v˜iL,whereMpv˜˜ij indicates the element’s average of vipj, and vip+1j, denotes the element’s standard deviation of vipj and viqj and viq+1j’s standard deviations are denoted by vip+1j, and Mpv˜˜ij=vipj+vip+1j/2,1≤p≤3,.Sqv˜ij=1/2∑k=qq+1vikj−1/2∑k=qq+1vikj2,1≤q≤3,Hpv˜ij denotes the.In the trapezoidal membership functionv˜ij,1≤p≤3,j∈U,L,and1≤i≤n., the membership value of the element vip+1j.Step 6: the CC(xj) closeness coefficient is then computed.(19)CCxj=d−xjd∗xj+d−xj.Step 7: alternatives can be ranked in descending order. The greater the value of CC(xj), the larger the green supplier’s preference.


## 4. Results

In Iran, fuzzy Best-Worst-TOPSIS assesses and ranks oil corporations based on their social responsibility component. In the research, three managers from oil firms serve as DMs. The environmental dimension is one of the elements being assessed. The social dimension, Economic dimension, Beneficiary dimension, Voluntary dimension, Ethical dimension, and Institutional dimension are based on three elements that influence supply chain price in oil companies: competitors, products, and customers. Table3 lists the assessment criteria that were established with the assistance of DMs. All of the assessment criteria in Table 3 are advantage criteria (B).Table 3
Components of social responsibility.
CoComponentsDefinitionsB1Environmental dimensionThe environmental component is concerned with the vulnerability of ecological and biophysical systems and their many functions in the event of a hazardous state, which causes damage and degradation. The corporate social responsibility (CSR) trend encourages businesses to go above and beyond legal requirements to contribute to environmental quality. Climate change and greenhouse gas emissions are frequently discussed.B2Social dimensionThe social dimension of social responsibility differs from the other components. It includes, among other things, the firm’s community performance methods as well as dynamic social relationships with stakeholders, such as data supply, performance transparency, and adaptable organizational structures.B3Economic dimensionThe economic part of corporate social responsibility relates to the distribution of resources to create products and services within the social system. In terms of the economy, social responsibility covers many elements of how companies influence the economy in terms of competition, consumers, employees, community, and environment.B4Beneficiary dimensionThis dimension is concerned with institutions and organizations’ commitment to serving the needs of stakeholders. It is, in fact, a set of management activities that ensure the company’s operations have the maximum potential positive influence on society. This viewpoint highlights the importance of CSR’s voluntary character.B5Voluntary dimensionBusiness frequently interprets the voluntary character of corporate social responsibility to indicate that, because corporate social responsibility activities are not obligatory, they are always discretionary and so maybe chosen exclusively by the business.B6Ethical dimensionWhile ethics is concerned with what is good and wrong in general, business ethics focuses on benefits for shareholders and stakeholders. On the other hand, social responsibility is concerned with the company’s influence on the environment and community. 4th of March 2018B7Institutional dimensionPast social responsibility/social performance study has done nothing to address the institutional drivers of social responsibility and its many expressions in terms of social performance in a systematic way. Instead, most of this study has focused on particular areas of the business-society interface, such as the link between social responsibility and economic success and the link between a firm’s internal features and its outward social performance.Each DM must first evaluate which parameters are the greatest and worst for him or her. Then, employing the linguistic variables given in Table2 compares them to other criteria (Tables 4 and 5). All DMs picked C1 as the best criterion, whereas C5 was chosen as the worst criterion.Table 4
Language preferences of decision-makers for the best criterion concerning each of the other factors (best-to-others vector).
CriteriaDM-1DM-2DM-3B1EMPEMPEMPB2SPSPVSPB3MPMPEMPB4SPVSPSPB5EPEPEPB6SPSPSPB7SPVSPSPTable 5
In the case of the worst criterion, decision-makers’ linguistic preferences for all other criteria (others-to-worst vector).
CriteriaDM-1DM-2DM-3B1EPEPEPB2VSPMPMPB3VSPVSPSPB4MPVSPVSPB5EMPEMPEMPB6VSPVSPMPB7VSPMPVSPThe language preferences of the DMs were translated to TFNs employing the scale in Tables (see Tables6 and 7). To obtain the fuzzy weights of criteria, Problem (7) was solved for each DM based on Table 8. Table 9 shows the ideal fuzzy criterion weights for each DM and the average fuzzy criteria weights. Average fuzzy weights are used in the F-TOPSIS phase. Equation (8) is used to derive related consistency indicators (ξ) for each DM, as given in Table 9. Because all of the consistency ratios are nearly 0, each DM’s criteria comparisons are highly consistent.Table 6
Applying TFNs, choose the best criterion for each of the other criteria (best-to-others vector).
CriteriaDM-1DM-2DM-3B1(7/2, 4, 9/2)(7/2, 4, 9/2)(7/2, 4, 9/2)B2(3/2, 2, 5/2)(3/2, 2, 5/2)(5/2, 3, 7/2)B3(2/3, 1, 3/2)(2/3, 1, 3/2)(7/2, 4, 9/2)B4(3/2, 2, 5/2)(5/2, 3, 7/2)(3/2, 2, 5/2)B5(1, 1, 1)(1, 1, 1)(1, 1, 1)B6(3/2, 2, 5/2)(3/2, 2, 5/2)(3/2, 2, 5/2)B7(3/2, 2, 5/2)(5/2, 3, 7/2)(3/2, 2, 5/2)Table 7
TFNs are used to prioritize all other criteria above the poorest criterion (others-to-worst vector).
CriteriaDM-1DM-2DM-3B1(1, 1, 1)(1, 1, 1)(1, 1, 1)B2(5/2, 3, 7/2)(2/3, 1, 3/2)(2/3, 1, 3/2)B3(5/2, 3, 7/2)(5/2, 3, 7/2)(3/2, 2, 5/2)B4(2/3, 1, 3/2)(5/2, 3, 7/2)(5/2, 3, 7/2)B5(7/2, 4, 9/2)(7/2, 4, 9/2)(7/2, 4, 9/2)B6(5/2, 3, 7/2)(5/2, 3, 7/2)(2/3, 1, 3/2)B7(5/2, 3, 7/2)(2/3, 1, 3/2)(5/2, 3, 7/2)Table 8
For the evaluation of options, linguistic variables and TFNs are used.
Linguistic variablesTriangular fuzzy numbers (TFNs)Very poor (VP)(0, 0, 1)Poor (P)(0, 1, 3)Medium poor (MP)(1, 3, 5)Fair (F)(3, 5, 7)Medium good (MG)(5, 7, 9)Good (G)(7, 9, 10)Very good (VG)(9, 10, 10)Table 9
For each decision-maker, fuzzy weights of 9 criteria and an associated consistency indicator (ξ), as well as average fuzzy weights.
Cr.DM-1DM-2DM-3Fuzzy weights (average)B1(0.039, 0.043, 0.043)(0.044, 0.049, 0.055)(0.048, 0.048, 0.048)(0.044, 0.047, 0.049)B2(0.047, 0.061, 0.076)(0.230, 0.230, 0.230)(0.039, 0.043, 0.043)(0.104, 0.111, 0.116)B3(0.100, 0.147, 0.154)(0.106, 0.137, 0.171)(0.070, 0.085, 0.099)(0.092, 0.123, 0.143)B4(0.055, 0.065, 0.081)(0.101, 0.114, 0.126)(0.099, 0.131, 0.147)(0.085, 0.097, 0.118)B5(0.169, 0.185, 0.185)(0.137, 0.137, 0.137)(0.176, 0.206, 0.206)(0.160, 0.120, 0.176)B6(0.089, 0.117, 0.154)(0.072, 0.086, 0.106)(0.101, 0.114, 0.126)(0.087, 0.105, 0.129)B7(0.106, 0.137, 0.171)(0.055, 0.065, 0.081)(0.107, 0.160, 0.160)(0.089, 0.120, 0.137)ξ(0.230, 0.230, 0.230)(0.672, 0.672, 0.672)(0.230, 0.230, 0.230)(0.377, 0.377, 0.377)DMs assess each alternative according to each criterion in the F-TOPSIS phase, using the linguistic variables in Table8. Table 10 shows the results of three DMs’ evaluations of the competitive supply chain pricing factors based on seven criteria. After linguistic terms are transformed to matching TFNs, the aggregated (average) fuzzy decision matrix is given in Table 11. Once the fuzzy normalized decision matrix is built, as illustrated in Table 12, the fuzzy weighted normalized decision matrix is computed. The distance from a fuzzy positive-ideal solution, the range from a fuzzy negative-ideal solution, and the proximity coefficients are all shown in Table 13. Customers, products, and competitors are the options rated from best to worst based on the values.Table 10
About each criterion, DMs’ judgments of the corporate social responsibility factor depending on competitive supply chain pricing in oil companies.
B1B2B3B4B5B6B7Competitors(7, 9, 10)(9, 10, 10)(7, 9, 10)(5, 7, 9)(9, 10, 10)(5, 7, 9)(9, 10, 10)(9, 10, 10)(9, 10, 10)(3, 5, 7)(9, 10, 10)(9, 10, 10)(7, 9, 10)(0, 1, 3)(9, 10, 10)(9, 10, 10)(5, 7, 9)(9, 10, 10)(9, 10, 10)(0, 1, 3)(1, 3, 5)Products(7, 9, 10)(5, 7, 9)(9, 10, 10)(7, 9, 10)(9, 10, 10)(7, 9, 10)(7, 9, 10)(0, 1, 3)(5, 7, 9)(9, 10, 10)(0, 1, 3)(1, 3, 5)(9, 10, 10)(7, 9, 10)(7, 9, 10)(5, 7, 9)(7, 9, 10)(7, 9, 10)(7, 9, 10)(9, 10, 10)(1, 3, 5)Customers(5, 7, 9)(7, 9, 10)(9, 10, 10)(1, 3, 5)(7, 9, 10)(3, 5, 7)(7, 9, 10)(7, 9, 10)(5, 7, 9)(9, 10, 10)(5, 7, 9)(1, 3, 5)(7, 9, 10)(9, 10, 10)(9, 10, 10)(7, 9, 10)(9, 10, 10)(7, 9, 10)(7, 9, 10)(7, 9, 10)(5, 7, 9)Table 11
Fuzzy decision matrixD˜.
B1B2B3B4B5B6B7Competitors(7.667, 9.333, 10.000)(5.000, 7.000, 8.667)(9.000, 10.000, 10.000)(5.000, 7.000, 8.667)(7.667, 9.333, 10.000)(3.667, 5.667, 7.667)(3.667, 5.667, 7.667)Products(8.333, 9.667, 10.000)(3.667, 5.667, 7.667)(5.000, 7.000, 8.667)(9.000, 10.000, 10.000)(5.000, 7.000, 8.667)(3.667, 5.667, 7.667)(8.333, 9.667, 10.000)Customers(7.667, 9.333, 10.000)(9.000, 10.000, 10.000)(7.667, 9.333, 10.000)(5.000, 7.000, 8.667)(5.000, 7.000, 8.667)(7.667, 9.333, 10.000)(3.667, 5.667, 7.667)Table 12
Weighted normalized fuzzy decision matrixV˜.
B1B2B3B4B5B6B7Competitors(0.039, 0.047, 0.049)(0.080, 0.138, 0.162)(0.134, 0.170, 0.176)(0.068, 0.109, 0.129)(0.134, 0.170, 0.176)(0.016, 0.144, 0.053)(0.031, 0.059, 0.085)Products(0.134, 0.170, 0.176)(0.016, 0.144, 0.053)(0.052, 0.104, 0.140)(0.079, 0.117, 0.129)(0.051, 0.077, 0.089)(0.059, 0.090, 0.120)(0.023, 0.048, 0.075)Customers(0.039, 0.047, 0.049)(0.152, 0.202, 0.210)(0.080, 0.138, 0.162)(0.044, 0.082, 0.112)(0.134, 0.170, 0.176)(0.059, 0.090, 0.120)(0.080, 0.138, 0.162)Table 13
Weighted normalized fuzzy decision matrix.
Alternatived-d+CCiRankingsCompetitors0.94248.02560.11923Products0.96558.23320.12012Customers0.97658.45830.12941The distance from fuzzy positive-ideal solution, distance from fuzzy negative-ideal solution, and proximity coefficients are computed as shown in Table13 and Figure 3. Customers, products, and competitors are the options rated from best to worst based on the values.Figure 3
Weighted normalized fuzzy decision matrix.


### 4.1. The Comparative Analysis

To verify the efficacy of the suggested strategy, it is compared to a benchmarking model. IT2F VIKOR is the subject of this benchmarking model. The significance weights of the Competitive supply chain pricing selection criterion are determined from BWM for both techniques. Both are used to rank Competitive factors in the model’s second phase. In our new benchmarking approach, Table14 provides the final scores and ranking patterns of competitive characteristics. Utilizing our recommended model (combined BWM and IT2F-TOPSIS), once the maximum group utility value (v) is modified to 0.5 or more than 0.5, the ranking orders of three Competitive factors remain identical, as seen in Table 14. This finding suggests that the suggested technique produces good results and provides appropriate outcomes for competitive factor ranking decision-making. According to the findings of this study, the three variables of competitive supply chain pricing based on customer, supply chain pricing based on products, and supply chain pricing based on competitive have the highest frequency.Table 14
Q values and rankings for each supplier in terms ofv value change.
Maximum group utility value (v) in VIKORQ valueRankCompetitorsProductsCustomersCompetitorsProductsCustomersv= 0.10.9850.4790.000321v = 0.10.9090.3390.000321v = 0.20.5600.8330.000231v = 0.30.6460.9450.000231v = 0.40.8700.6380.000321v = 0.50.7250.3720.000321v = 0.60.6930.3870.000321v = 0.70.4890.8650.000231v = 0.80.8900.4750.000321v = 0.90.6550.3400.000321v = 1.00.7830.4390.000321


## 4.1. The Comparative Analysis

To verify the efficacy of the suggested strategy, it is compared to a benchmarking model. IT2F VIKOR is the subject of this benchmarking model. The significance weights of the Competitive supply chain pricing selection criterion are determined from BWM for both techniques. Both are used to rank Competitive factors in the model’s second phase. In our new benchmarking approach, Table14 provides the final scores and ranking patterns of competitive characteristics. Utilizing our recommended model (combined BWM and IT2F-TOPSIS), once the maximum group utility value (v) is modified to 0.5 or more than 0.5, the ranking orders of three Competitive factors remain identical, as seen in Table 14. This finding suggests that the suggested technique produces good results and provides appropriate outcomes for competitive factor ranking decision-making. According to the findings of this study, the three variables of competitive supply chain pricing based on customer, supply chain pricing based on products, and supply chain pricing based on competitive have the highest frequency.Table 14
Q values and rankings for each supplier in terms ofv value change.
Maximum group utility value (v) in VIKORQ valueRankCompetitorsProductsCustomersCompetitorsProductsCustomersv= 0.10.9850.4790.000321v = 0.10.9090.3390.000321v = 0.20.5600.8330.000231v = 0.30.6460.9450.000231v = 0.40.8700.6380.000321v = 0.50.7250.3720.000321v = 0.60.6930.3870.000321v = 0.70.4890.8650.000231v = 0.80.8900.4750.000321v = 0.90.6550.3400.000321v = 1.00.7830.4390.000321


## 5. Discussion

The current situation of the global economy is such that most countries are attempting to acquire a competitive position at the international level. Traditionally competition is a static aspect and success or failure in it as production is dependant. But in today’s modern world, competition is a dynamic force, and new product technologies, new market borders, new production methods, and new management ideas contribute to the development, alteration, or even extinction of the advantage of national industrial competitiveness. The history of countries such as China and Japan reveals that the only reason for their success is their capacity to exceed the idea of static competitive advantages. The aforementioned nations were able to overcome the restrictions of their resources via scientific and technical advances, expanding the volume of production and growth of foreign commerce. These countries recognized that comparative advantage could be established by increasing skills, capital, and labor, and that structural change could create an environment favorable to economic progress. Today, competitive strategy and its key branches of industry analysis, competitor analysis and strategic positioning are viewed as an acknowledged aspect of management activity. Competitive strategy has offered a comprehensive framework for examining the fundamental determinants of rivalry in industry. As a result, the goal of this study is to look at the most efficient operation of competitive supply chain pricing focused on social responsibility dimensions with a case study of oil products from knowledge-based enterprises.


## 6. Conclusion and Future Works

With competitive supply chain pricing, corporate social responsibility has become the most efficient business strategy in the globe. Since most firms utilize these performances or acquire information about their businesses to evaluate and choose the best criterion. In an MCDM challenge, a competent Supply Chain Pricing considerations solutions supplier considers multiple possibly competing for qualitative and quantitative criteria. This study presented a new extended decision making and FBWM method based on Type-2 Fuzzy Sets TOPSIS for ranking the corporate social responsibility criteria based on competitive supply chain pricing factors. The aim of the ranking is to learn how a company and its supply chain may accomplish joint-optimization of economic, environmental, and social performance by transparently and strategically integrating sustainability into business operations. The most effective functioning of competitive supply chain pricing in knowledge-based businesses’ oil supplies was investigated in this research, which concentrated on social obligation components. Three functions of competitive supply chain pricing and seven propositions connected to the dimensions of social responsibility in knowledge-based businesses in the oil sector were established depending on the research results to meet the first and second research goals. The most plausible dimension of social responsibility in knowledge-based businesses in the oil sector, according to the research, is the environmental dimension in social responsibility, which may produce competitive supply chain pricing mechanisms based on profitability. The environmental factor, which refers to the establishment of professional environment procedures in keeping with the nature of knowledge-based companies based on the production of knowledge-based commodities, shapes corporate social responsibility methods. It keeps them accountable to stakeholders and enhances social perceptions of their performance to more fully integrate the national and domestic economies. Since oil is a vital product in our nation’s economy, we focus on development-oriented characteristics in the oil sector and its derivatives. It promotes trust and confidence in firms that utilize such items as primary materials or raw materials, in addition to adhering to environmental standards. It promotes long-term economic growth and thriving. In other findings; it was revealed that the elements of Competitive Supply Chain Pricing factors in Oil Companies based on social responsibility are ranked as follows: customer, product, and competitive supply chain pricing procedures. As a result, maintaining customer values is a major price element in establishing contacts, as determined by the company’s strategic connection with the economy, as well as regional and worldwide partners. In actuality, this price function attempts to persuade clients to use the goods of domestic and Iranian knowledge-based businesses by earning their trust and confidence to create value for their customers. Knowledge-based companies will be able to assess their competitive position in this circumstance strategically, based on value-based competitive supply chain pricing, and plan for likely future scenarios, and sell items to meet customer wants. To gain consumers’ trust, the first step to success should involve the significance of professional ethics, in addition to knowledge. In these circumstances, a pricing strategy that focuses on establishing inclusive values for customers may strengthen a company’s competitive position and position it at the forefront of strategic connections with energy-using companies. Also, Access to specialists created problems such as prolonging the research process.


### 6.1. 1- Suggestions for Future Research

Although the model of this research is designed for the energy industry; considering the basic similarities between energy industries such as petrochemicals and refineries, its generalizability can also be examined. It also seems necessary to conduct data-driven research to model the competitive supply chain in NGOs and medium knowledge-based companies.


## 6.1. 1- Suggestions for Future Research

Although the model of this research is designed for the energy industry; considering the basic similarities between energy industries such as petrochemicals and refineries, its generalizability can also be examined. It also seems necessary to conduct data-driven research to model the competitive supply chain in NGOs and medium knowledge-based companies.



---

*Source: 7879028-2022-04-30.xml*