Competitive strategy selection in retail E-Commerce using IF AHP & TOPSIS methodology

Abstract

Retail e-commerce has been growing over the years by attracting entrepreneurs’ attention. Increasing interest in retail e-commerce has affected competition and made it necessary to choose the right competition strategy. This study aims at selecting the right competition strategy to be successful in retail e-commerce under vague and imprecise conditions. AHP and TOPSIS methods are used under intuitionistic fuzzy environment, which allows decision-makers to reflect their hesitation in their judgements. After a literature review on retail, e-commerce, and competition strategy, the main criteria and sub-criteria of multicriteria retail e-commerce alternative selection are presented. Cost leadership, differentiation, and focusing strategies are evaluated as competitive strategies, based on the opinions of experts collected through a questionnaire. The results of the study show that the strategies are ranked as differentiation, focus, and cost leadership strategies, respectively.

Keywords

Decision making intuitionistic fuzzy competitive strategy retail e-commerce

1 Introduction

The world retail trade, which grew by 6.2% in 2017 and reached a volume of $ 22.9 trillion, reached $ 25 trillion by increasing 4.5% in 2019. World retail trade is expected to grow by an average of 4% annually, reaching $ 29.7 trillion in 2023. The world online retail business, which grew 28% in 2017 and reached $ 2.4 trillion, was estimated to have increased by 20.7% in 2019 and reached $ 3.5 trillion. The share of online retail trade, which had a 10.4% share in the world retail trade in 2017, is expected to increase to 22% in 2023 with $ 6.5 trillion [1].

In Turkey, the online retail volume reached $ 7.9 billion in 2019. Thus, the share of online retail in retail was 6.2%. This rate was 12.3% in developed countries and 6.7% in developing countries [2]. In the world, retail e-commerce is expected to grow by a compound annual growth rate of 8.1% between 2020 and 2023. Turkey has the highest growth value rate of 20.2% in the same period [3]. This data indicates the retail e-commerce growth in the world and Turkey.

The Union of Chambers and Commodity Exchanges of Turkey publishes company establishment and liquidation statistics in Turkey. The top ten operating areas are established according to the number of companies and the top ten working areas closed are also listed. In this data, statistics of joint-stock companies, limited companies, and sole proprietorships are provided separately. The data, the period of December 2015 to May 2020, were examined. It was assumed that companies whose field of activity was “retail trade by mail or over the internet” constituted the data of retail e-commerce companies. Apart from this, companies not formally established or whose activities are in different areas of retail can do e-commerce. However, the area we examined is crucial in that it reflects the general trend.

By sort of activity areas of the sole proprietorships, without any interruption from January 2018 to May 2020, the activity area of retail e-commerce enters the top ten areas of newly established companies. By sort of activity areas of the joint-stock companies, without any interruption from November 2019 to May 2020, the activity area of retail e-commerce enters the top ten areas of newly established companies. In the limited company type, it was listed only in May 2019, April 2020, and May 2020. The closed company statistics were list cumulatively. By sort of activity areas of the joint-stock companies, during the 2019 and January to May 2020, the activity area of retail e-commerce enters the top ten areas of closed companies [4]. Established and closed company statistics in Turkey show the interest of people to the retail e-commerce activities forehead.

All the data mentioned above indicate a growing market and increasing interest. This position raises an important question. Which competition strategy should companies adopt, and where should they focus on developing their companies? This study aims to find solutions to those problems. For this purpose, literature research was carried out in retail, e-commerce, competitive strategy and other related areas.

In order to choose the appropriate competitive strategy, the opinions of people who have expertise in the field of retail, e-commerce, and strategy were obtained through a questionnaire. Fuzzy decision-making methods allow the experts to evaluate the opinions expressed in linguistic expressions. In addition, intuitionistic fuzzy methods were preferred in this study because they also take into account the hesitations of the experts in their opinions.

Zadeh [5] introduced the fuzzy set theory in 1965. The sum of degrees of membership and non-membership in an ordinary fuzzy set has to be equal to 1. However, this sum may be less than 1 due to lack of information in real life. Atanassov has developed intuitionistic fuzzy sets for this situation [6]. In this article, intuitionistic fuzzy sets are used as a decision-making tool under uncertainty. This method allows decision-makers to evaluate their linguistic expressions under vagueness and impreciseness. The use of intuitionistic fuzzy sets in the decision-making model allows the evaluation of decision-makers’ degree of membership, non-membership, or hesitation in a set. This study aims to choose the right path (strategy) to achieve the desired goal (to be successful in retail e-commerce). It makes sense to decide which path to take at the beginning of the road.

This study takes expert opinions into account to determine the right strategy for retail e-commerce. Expert opinions on which strategy to choose are personal qualitative information. The use of intuitionistic fuzzy sets in incorporating the opinions of experts expressed in linguistic expressions into the decision-making model enabled the consideration of personal qualitative information. The originality of this study is the evaluation of competitive strategies in a sector with high interest to retail e-commerce by using an intuitionistic fuzzy AHP&TOPSIS methodology. According to the research in the accessible literature, it is the first study in which intuitionistic fuzzy AHP and TOPSIS methods were used in the selection of competitive strategies in retail e-commerce.

The rest of the paper is organized as follows. The section 2 presents a literature review on the concepts of retail, e-commerce and competition strategies. The section 3 gives the intuitionistic fuzzy AHP&TOPSIS methodology. The section 4 presents the case study on retail e-commerce strategy selection. The last section includes the conclusions and directions for further research.

2 Main concepts and literature review

Literature research was carried out for the retail, e-commerce, competition strategy, and intuitionistic fuzzy decision-making model. As a result of this step, the main criteria, sub-criteria, and alternatives were determined.

2.1 Retail

This section describes the definition of retail and which transactions are considered retail. This part includes studies on the criteria of success in retailing. Thus, resources were provided for the criteria and sub-criteria to be used in the study.

Retailing is the final step in the complex process of delivering products and services as desired, at the time and place of the end consumer. Retailing covers the purchase and sale of goods and services and all related activities. The retailer performs its businesses by researching the world markets, choosing the appropriate goods for the customer, buying, selling, and presenting it to the customer where he or she wants [7]. Retailing includes all activities related to the sale of products and services for personal and non-business use of end consumers. Many organizations, including manufacturers, wholesalers, and retailers, operate as retailers. However, most of the retailing are done by organizations, the main business of which is retailing. Retailing plays an essential role in many marketing channels [8]. Some retailers operate from stores, while others are in service without shops, online, on TV, printed catalogues, vending machines, and even in consumers’ homes. Most retailers sell physical goods produced by others. However, retailers are also manufacturers in retail service businesses such as dry cleaning, fast food, or online bank accounts. Even the largest retailers may have trouble dealing with small transactions as they serve individual consumers. Compared to those who operate at other channel levels, the total number of retailers’ transactions with consumers is much higher [9]. Retailing refers to the process of purchasing and selling goods without making significant changes to the product, except for providing some service to the end customer. Despite this traditional definition, retailers have become complicated companies that often coordinate the entire value chain from production to the sales stage [10]. Retailers have been an essential channel in collecting information in the technologically developing earth. This enables manufacturers to receive feedback on consumers’ habits, tendencies, demands, complaints, and many other issues through retailers [11]. Retailers are organizations that perform the function of distributing goods and services by adding values in the time, property, and form functions in the marketing mix consisting of product, price, promotion, and distribution [12].

After the definition of retail, it will be appropriate to mention the areas that are included in the scope of retail. Turkey Statistical Institute has classified retail as food-drink and tobacco, non-food (except automotive fuel), and automotive fuel [13]. The consulting firm Deloitte has classified the retail sector as apparel and accessories, fast-moving consumer goods, hardline and leisure goods, and diversified [14]. Another consulting firm KPMG has classified the retail industry as various goods services, restaurants-hotels, education services, entertainment-culture, communication, transportation, health, furniture-home appliances-maintenance services, housing-rent, clothing-shoes, alcoholic drinks-cigarettes-tobacco, and food-soft drinks [15]. Fast-moving goods, which have an essential place in retail, mean cereals, grain, wheat, vegetables, fruits, meat, fish, soft drinks, alcoholic beverages, dairy products, food products [16]. The expression of fast-moving consumer goods is used for home cleaning & personal care, food & beverage, and health products [17].

Four online shopping sites were examined in order to determine under which titles the retail websites offer the products to their customers. In Table 1, we are analyzing the product classification of the N11 [18], Hepsiburada [19], Amazon Turkey [20], and Gittigidiyor [21].

Table 1
Online marketplaces main product classification

N 11 Hepsiburada Amazon Turkey Gittigidiyor

Clothing and Shoes Electronic Fashion Electronic

Electronic Fashion Book Home, Garden, and Office

Home and Life Home, Life, Stationery, Office Electronic Fashion

Mom and Baby Auto, Garden, Construction Market Computer and Office Cosmetics, Personal Care

Cosmetics and Personal Care Mom, Baby, Toy Telephone Baby, Mom

Jewelry and Watch Sports Outdoor Home and Kitchen Supermarket

Sports and Outdoor Cosmetic Personal Care Personal care Hobby, Entertainment, and Art

Books, Music, Movies, Games Supermarket, Pet Shop Construction market Cars, Motorcycles

Tickets, Holidays and Entertainment Books, Music, Movies, Hobbies Video Game and Console Sports, Outdoor

Automotive and Motorcycle Baby and Toy

Sports and Outdoor

N 11	Hepsiburada	Amazon Turkey	Gittigidiyor
Clothing and Shoes	Electronic	Fashion	Electronic
Electronic	Fashion	Book	Home, Garden, and Office
Home and Life	Home, Life, Stationery, Office	Electronic	Fashion
Mom and Baby	Auto, Garden, Construction Market	Computer and Office	Cosmetics, Personal Care
Cosmetics and Personal Care	Mom, Baby, Toy	Telephone	Baby, Mom
Jewelry and Watch	Sports Outdoor	Home and Kitchen	Supermarket
Sports and Outdoor	Cosmetic Personal Care	Personal care	Hobby, Entertainment, and Art
Books, Music, Movies, Games	Supermarket, Pet Shop	Construction market	Cars, Motorcycles
Tickets, Holidays and Entertainment	Books, Music, Movies, Hobbies	Video Game and Console	Sports, Outdoor
Automotive and Motorcycle		Baby and Toy
		Sports and Outdoor

It will also be useful to mention the concept of the channel without going through the literature information on success criteria in retail. The concept of a channel is defined as a customer contact point or an interaction tool. In the academic field, omnichannel is defined as the fact that customers shop anywhere and anytime. Some call it merely multi-channel [22]. In the last decade, business models have changed with mobile channels and social channels in the marketing world. A movement is observed from the multi-channel structure to the omnichannel structure. The omnichannel extensively addresses customers’ movement across channels in search and purchase decisions [23]. In this digital age, the dynamics of retail are determined by the omnichannel structure [24].

What is evaluated as a success criterion in the retail sector has been examined in the literature. In general, there is a situation that can be summarized as financial and non-financial.

Some of the financial success criteria are return on net worth [25], sales targets, growth, stability, rate of sales, the return of capital employed and the growth index in price/earnings ratio [26], profitability rates, operational rates, operating rates, financial structure rates, and liquidity ratios [27].

Some of the non-financial success criteria are establishing long-term relationships with customers [12], total retail experience, customer service, and relationship retailing [28], transport optimization, information technology optimization, stock optimization and resource optimization [29], improvement of supplier infrastructure [30, 31], word of mouth [32], individual bargaining [33], quality, price-performance and customer service [27], customer benefits [12], lifelong customer value, active/passive customer ratio, multi-channel structure and support of channels [24].

2.2 E-commerce

Another issue that constitutes this study is e-commerce. In this section, the definition, types, and business model of e-commerce are mentioned. Also, at the end of this section, studies on e-commerce success and performance criteria are included. These are used to determine the sub-criteria and criteria used in this study.

E-commerce is the realization of commercial transactions between individuals and organizations digitally. It requires the use of the internet, web, mobile applications, mobile browsers for the creation of e-commerce business [34]. E-commerce is the purchase, sale, transportation, or data, goods, and service trade using the internet and other networks [35]. The World Trade Organization defines e-commerce as “production, distribution, marketing, sales and delivery of products and services using electronic instrument” [36].

Types of e-commerce are business to consumer, business to business, customer to customer, mobile e-commerce, social e-commerce, and local e-commerce [34]. In the type of business to consumer e-commerce, transactions occur between the end-user of products and services and the business that provides these products and services [37]. While the online retail business is called e-tailing, those who do this job are called e-tailers [38].

Another issue in e-commerce is the business model. Timmers has defined the concept of the business model for e-commerce as a structure for product, service, and information flow, an explanation of potential benefits for various business actors, and an explanation of income sources [39]. Successful business models reveal a better way of doing business than existing business models. It offers more value for different customer groups or brings new generation business styles instead of past business ones [40]. It is worth mentioning the difference between the business model and the strategy. While the strategy focuses on how to dominate competitors, the business model shows the logic of creating value and effective coordination of business resources [41].

In this section, the studies conducted under the title of e-commerce success and performance criterion are mentioned. In the study in which success factors for e-commerce were evaluated in Thailand, it was revealed that trust and shopping habits were the critical success factor [42]. In the survey conducted specifically for France e-commerce markets, it was determined that the main essential elements of success were time savings, shopping whenever desired, little physical activity required, and useful and rational purchasing factors increased the motivation of purchasing. It has been stated that the complex website perception, risk of product quality, limited options, the monotonous purchasing experience, incomplete delivery, improperly placed products, and lack of social connectivity have also caused the purchase not to be repeated [43]. Most of the success factors of physical retailing also fit retail e-commerce. Measurability and safety are important issues. Unlike traditional retailers, e-retailers can offer special customer service [38]. Globally, the most significant advantage of e-commerce is the ability to do business anytime, anywhere, and at reasonable costs. Verifying the identity of the buyer and seller, trust, order fulfilment, delivery, and security are the most significant barriers. For the small and medium enterprises, coping with many of the products, lack of knowledge or IT expertise, and awareness of opportunities and risks associated with them make it difficult to create an e-commerce strategy. However, small and medium enterprises can use marketplaces, such as Alibaba and Amazon, to sell their products [44]. In retail e-commerce, there are few restrictions on market entry, so competition is enormous. It is difficult to survive and be profitable without a brand or experience. Trying to reach every online customer quickly consumes resources. Developing a niche market strategy and defining the target market and requirements should be made to generate profit. Keeping costs low, a lot of alternatives and stock control are critical factors for success. Inventory control is the most crucial criterion [45]. The main reasons for the customers to make shopping repeatedly on membership-based private shopping sites consist of the high quality of the products and the discounted prices, saving time, convenience, and product variety. The main reasons for not shopping again are economical, loss of time, lack of price advantage, and defective product, poor quality, or non-original products [46].

Performance indicators of retail websites are sales/conversions, the number of inquiries, customer satisfaction surveys, the return on investment, increase in customer loyalty. Intangible performance indicators are consist of awards, visitor comments, e-mail surveys, brand awareness, user studies, competitor benchmarks, and in-store research [47]. Five indicators have been determined to measure the marketing performance of internet companies. These are the international ranking indicating the importance of the website, the opening speed of the page showing the quality of the website, the links given to the website showing the interaction of the website with other sites, the time spent per person per day showing the user experience and the number of unique visitors showing the traffic of the website. Also, the two financial performance indicators determined are the business-based revenue and the revenue market share as showing the place in the competition [48].

2.3 Competition strategies

The other component of the study is the competition strategies that will determine alternatives. Briefly mentioning the definition of strategy, and then literature information about the competition strategy will be given.

Defining the strategy has been likened to blind people holding a different part of the elephant and identifying the elephant [49]. The strategy can be defined as the chosen business way, preparation for situations that can lead to positive or negative results, determining tactics, and the vital function of the organization [50].

Some of the methods developed to gain a competitive strategy in businesses are McKinsey matrix, Porter’s competitive strategy, Boston consulting group’s product portfolio matrix, product life cycle, and market/competition matrix [51]. Porter’s differentiation, cost leadership, focus, and their combination are strategy typologies that are widely accepted, discussed, and preferred by companies [52]. In addition to Porter’s classification, asset parsimony [53], scale, and customer loyalty [45] can also be added.

The competitive strategy is determining the firm’s position in the sector. Positioning determines whether the company’s profit is above or below the sector. Long-term above-average industry yield is possible with a sustainable competitive advantage. Although firms have numerous strengths and weaknesses compared to their competitors, basically they have two competitive advantages, low cost, and differentiation. Two primary competitive advantages can be gained through three general strategies. These are cost leadership, differentiation, and focus. Focusing is divided into two as cost and differentiation [54]. The following paragraphs explain three competitive strategies: cost leadership, differentiation, and focus.

Cost leadership is based on manufacturing at the lowest cost in the industry. The company operates on a wide scale and can work in many segments of the sector as well as in the related sectors. Wide scale is essential for cost advantage. The source of the cost advantage is diverse and depends on the industry. The economic scale can be achieved for reasons such as proprietary technology and privileged access to raw materials. Low-cost manufacturers often sell standard products and target a certain scale [54]. Cost leadership is to concentrate on producing the product or service inexpensively than its competitors. In addition to the need for efficient scale facilities, companies try to reduce costs in production and keep product R&D, services, sales, and marketing expenses to a minimum. The implementation of innovation is delayed and imitated. Generally, differentiation and cost leadership are not applied at the same time, non-effective the advantages of the two strategies in an interim solution [53]. The cost leadership strategy aims to offer products that take account of average customer needs in a broad target market. The primary basis for this is the view that cost can be reduced with a standard product [51]. The cost competition strategy is to obtain opportunities that the company cannot access in the marketplace as a process or resource. Moving production to low-paying locations is an example of cost reduction. Competition over cost is short-term and unreliable. Other competitors can find situations that allow lower costs [45].

In its differentiation strategy, the company aims to be unique in some parts of the sector, which seems valuable to the customer. The firm positions itself to uniquely meet one or more features that the customer and industry see as valuable. Uniqueness makes a high price possible. Differentiation is various for each sector. Differentiation consists of broad factors such as the product itself, delivery system, and marketing approach. The cost of differentiation should be lower than the profit from the differentiated product and should consider the price of similar and close products of competitors. For the high price, the company must offer something truly unique or perceived as such. Differentiation strategy can be more than one in the sector as opposed to cost leadership since many features are valuable by customers [54]. The differentiation strategy is to differentiate product or service and present the products to the customer at a high price and in a way that will provide income above the market [51]. Differentiation is the operation to make manufacturers’ products and services unique and to distinguish them from other competitors. Generalization is the opposite of differentiation, and the only factor in choosing is the price. If the only difference in the product is the price, the price of the product decreases up to the cost price. To solve this, it is the creation of a monopole-like situation, which is the only supplier, by differentiating the product and service. Differentiation can be achieved by developing products and services based on customer experience, adding new features to the product, and products and services that have the features to solve customer problems. E-commerce offers unique differentiation for products and services. Differentiation provides personalized shopping, customizing the product and service, shopping from home or work, giving accessibility to from anywhere in the world, supplying wealth and interaction with user comments and videos, and accessing detailed information of the product [45].

Focusing is slightly different from other strategies, as it considers a narrower scale of competition in the industry. Focusing strategy keeps in mind segments or segment groups that other companies in the industry do not consider. Focusers should concentrate on gaining competitive advantage in their target segments, although not all segments apply. There are two types of focus strategies. While the focus of cost concentrates on the cost advantage in the target segment, the focus of differentiation puts the centre on differentiation in the target segment. Both types of focusing strategies aim to meet the particular needs of buyers in the target segment and to differentiate by providing the best service in terms of production or delivery. Focus strategy appliers can compete with rivals who are targeting a broader market with sub-optimizations. While competitors cannot provide enough performance to meet the needs of a particular segment, focusing can give this opportunity. Those who target a broad market can provide services above the needs of a specific market; in this case, they can increase the costs for the segment [54]. The focus strategy attempts to obtain it by concentrating on a particular market or a specific section of the market [51]. The strategy of focusing on niche markets addresses competition in a narrow market or product segment. It can implement in a very small segment with personalization and customization [45].

2.4 Other Issues

This chapter includes additional issues, which are the general environment, competition in the industry, and the concept of value that can affect the study.

The environment is the industry where the firm competes against rivals. The environment determines competitive actions and responses for firms to outperform their competitors and achieve above-average returns. The general environment consists of dimensions that affect the sector and the company. These dimensions include demographic, economic, political/legal, sociocultural, technological, globality, and physical sustainability. Companies cannot directly affect the external environment, but they can predict trends and effects. To be successful in the changes in the market, companies should use the expected trends to predict their impact on their ability to determine the strategy [55].

Competitive forces in the industry affect the strategy. Porter has identified five powers, including the new competitor threat, the bargaining power of customers, the bargaining power of suppliers, the substitution service or product threat, and the sharpness of competition in the industry [56].

For the customer, value is the customer’s perception of the value chain. Value is the perception of the benefit received against the price paid [28]. Companies use their talents and core competencies to create value. Firms with competitive advantage generate more value for customers than their competitors [57]. Porter and Millar have stated that the value created by a company is measured by the amount that buyers are willing to pay for a product or service. When the value created by a company exceeds the cost of generating this value, the company is in profit [58].

2.5 Fuzzy literature on competitive strategy selection

The fuzzy sets were put forward by Zadeh. The degree of membership for each object for the fuzzy sets ranges from 1 to 0 [5]. There is a difference between probability and fuzzy. Probability is related to whether or not to be a member of uncertainty in a non-fuzzy set. Fuzziness, on the other hand, mentions that there may be degrees between being a full member and not being a full member. When we talk about the company loss probability of 0.8, we talk about a possibility. When we talk about the modern look of company X, the uncertainty of the term of modern look constitutes the fuzzy of the set [59]. Atanassov developed the intuitionistic fuzzy sets by adding the degree of hesitation to the fuzzy set he defined with Zadeh’s membership degree and non-membership degree [60].

Since intuitionistic fuzzy sets are defined by the degree of membership, degree of non-membership, and degree of hesitation, they can be more suitable than fuzzy sets in the environment of uncertainty and better meet people’s thoughts in real life [61].

After the literature search, the authors have not found literature studies on competition strategy selection with intuitionistic fuzzy decision-making methods in the retail sector. But, different fuzzy decision-making methods have been used in the selection of strategies in the literature. As an example, sustainable risk management strategy selection [62], innovative capacity-based approach to blue ocean strategies of family firms [63], organizational strategy development in distribution channel management [64], creation of organizational strategies [65] and prioritization of production strategies of a manufacturing [66] can be given.

Studies using the intuitionistic fuzzy method in the retail sector have been examined in the literature. Some of the relevant studies are in the form of investigating customer satisfaction in electronic retail [67], the survey on retail store performance [68], and the evaluation of retail industry performance [69].

In the literature, studies in which the intuitionistic fuzzy decision-making method is used together with the competition, competitive strategy or related topics are limited. Some of these are resilient supplier selection [70], supplier selection [71] and aftersales performance evaluation [72]. Also, the studies conducted in recent years are in Table 2.

Table 2
Usage of IF and IVIF with AHP and/or TOPSIS method in last years

Year Author(s) Method Topic Journal Reference

2021 Nakiboğlu &Bulgurcu Intuitionistic fuzzy TOPSIS Supplier selection in textile company The Journal of The Textile Institute [73]

2021 Lu, Tian &Buitrago AHP and Intuitionistic fuzzy TOPSIS Chinese government venture capital investment evaluation for star-up Managerial and Decision Economics [74]

2020 Rouyendegh, Yildizbasi & Üstünyer Intuitionistic fuzzy TOPSIS Green supplier selection Soft Computing [75]

2020 Dogan, Deveci, Canitez &Kahraman Interval-valued intuitionistic fuzzy AHP and TOPSIS A corridor selection for locating autonomous vehicles Soft Computing [76]

2020 Kahraman, Öztayşi &Onar Integrated intuitionistic fuzzy AHP and TOPSIS Evaluation of outsource manufacturers De Gruyter [77]

2020 Luo, Chen, Cen, Pan, Zhong &He Intuitionistic fuzzy AHP Environmental impact assessment model for shale gas development Journal of Engineering, Design and Technology [78]

2020 Büyüközkan, Havle &Feyzioğlu Interval-valued intuitionistic fuzzy AHP Digital service quality model and its strategic analysis Journal of Air Transport Management [79]

2020 Tiwari, Lohani &Muhuri Interval-valued Intuitionistic fuzzy TOPSIS Automobile company supplier selection 2020 IEEE International Conference on Fuzzy Systems [80]

2020 Kumar &Anbanandam Intuitionistic fuzzy AHP-TOPSIS Location selection of multimodal freight terminal with sustainability criteria Research in Transportation Business &Management [81]

2019 Memari, Dargi, Jokar, Ahmad &Rahim Intuitionistic fuzzy TOPSIS Sustainable supplier selection form automotive manufacturer Journal of Manufacturing Systems [82]

2019 Büyüközkan, Feyzioğlu &Havle Intuitionistic fuzzy AHP Strategic service quality analysis for digital hospitality Industry IFAC (Conference) [83]

2019 Kumar &Anbanandam Intuitionistic fuzzy AHP and TOPSIS Freight terminal selection consider sustainability Research in Transportation Business &Management [84]

Year	Author(s)	Method	Topic	Journal	Reference
2021	Nakiboğlu &Bulgurcu	Intuitionistic fuzzy TOPSIS	Supplier selection in textile company	The Journal of The Textile Institute	[73]
2021	Lu, Tian &Buitrago	AHP and Intuitionistic fuzzy TOPSIS	Chinese government venture capital investment evaluation for star-up	Managerial and Decision Economics	[74]
2020	Rouyendegh, Yildizbasi & Üstünyer	Intuitionistic fuzzy TOPSIS	Green supplier selection	Soft Computing	[75]
2020	Dogan, Deveci, Canitez &Kahraman	Interval-valued intuitionistic fuzzy AHP and TOPSIS	A corridor selection for locating autonomous vehicles	Soft Computing	[76]
2020	Kahraman, Öztayşi &Onar	Integrated intuitionistic fuzzy AHP and TOPSIS	Evaluation of outsource manufacturers	De Gruyter	[77]
2020	Luo, Chen, Cen, Pan, Zhong &He	Intuitionistic fuzzy AHP	Environmental impact assessment model for shale gas development	Journal of Engineering, Design and Technology	[78]
2020	Büyüközkan, Havle &Feyzioğlu	Interval-valued intuitionistic fuzzy AHP	Digital service quality model and its strategic analysis	Journal of Air Transport Management	[79]
2020	Tiwari, Lohani &Muhuri	Interval-valued Intuitionistic fuzzy TOPSIS	Automobile company supplier selection	2020 IEEE International Conference on Fuzzy Systems	[80]
2020	Kumar &Anbanandam	Intuitionistic fuzzy AHP-TOPSIS	Location selection of multimodal freight terminal with sustainability criteria	Research in Transportation Business &Management	[81]
2019	Memari, Dargi, Jokar, Ahmad &Rahim	Intuitionistic fuzzy TOPSIS	Sustainable supplier selection form automotive manufacturer	Journal of Manufacturing Systems	[82]
2019	Büyüközkan, Feyzioğlu &Havle	Intuitionistic fuzzy AHP	Strategic service quality analysis for digital hospitality Industry	IFAC (Conference)	[83]
2019	Kumar &Anbanandam	Intuitionistic fuzzy AHP and TOPSIS	Freight terminal selection consider sustainability	Research in Transportation Business &Management	[84]

3 Model

The study aims to choose a competitive strategy in retail e-commerce. For this, intuitionistic fuzzy AHP and TOPSIS decision making models were used. The criteria and alternatives were determined by taking the literature study and expert opinion are given below.

As a result of the literature study mentioned above, criteria and sub-criteria were determined. For the determined criteria, revisions were made by getting opinions from experts. Accordingly, three main criteria and 11 sub-criteria are defined (Table 3). Competition strategy alternatives are cost leadership (A1), differentiation (A2), and focus (A3).

Table 3
Main criteria and sub-criteria

(M1) Environment (M2) Operation (M3) Customer

(C1) Competitive environment (C4) Value chain integration (C7) The customer’s internet usage skills and experience

(C2) Demographic, cultural and economic environment (C5) Convert resources to competitive advantage (C8) Website facilities and image

(C3) Legal environment (C6) Business model (C9) Expected benefit

(C10) The shopping experience of the customer

(C11) The presence of supporting channels

(M1) Environment	(M2) Operation	(M3) Customer
(C1) Competitive environment	(C4) Value chain integration	(C7) The customer’s internet usage skills and experience
(C2) Demographic, cultural and economic environment	(C5) Convert resources to competitive advantage	(C8) Website facilities and image
(C3) Legal environment	(C6) Business model	(C9) Expected benefit
		(C10) The shopping experience of the customer
		(C11) The presence of supporting channels

The decision-making model consists of two parts. With the IFAHP, the criteria and sub-criteria are weighted, and alternatives are compared with IF TOPSIS.

Thomas Saaty developed the AHP in 1971. This approach allows multiple people, multiple criteria, multi-stage complex problems to be solved hierarchically [85]. AHP includes both rating and comparison methods [86]. TOPSIS, developed by Hwang and Yoon, is the compromise solution based on choosing the solution with the Euclidean distance closest to the ideal solution and the Euclidean distance farthest from the negative ideal solution [87].

The intuitionistic fuzzy AHP where the criteria weights are determined and the TOPSIS method, which compares the alternatives, constitutes the main two steps. These two steps consist of eight stages.

Step 1: IFAHP

X is a set and is defined A ⊂ X, in the intuitionistic fuzzy set $\tilde{A}$ ;

$\tilde{A} = {(x, μ_{A} (x), υ_{A} (x)) | x \in X}$ (1) degree of membership μ_A : E → [0, 1], degree of non-membership υ_A : E → [0, 1] should define A set for all x ∈ X. All x ∈ X provides 0 ⩽ μ_A + υ_A ⩽ 1. For each intuitionistic fuzzy $\tilde{A}$ set is defined in X, the degree of hesitation is defined below as x ∈ X [60].

$π_{A} (x) = 1 - μ_{A} (x) - υ_{A} (x)$ (2)

It is in range 0 ⩽ π_A (x) ⩽1 for each x ∈ X. The π_A (x) value decreases as the accuracy of the information about x increases; the π_A (x) value increases as the accuracy of the information about x decreases [88].

The intuitionistic preference relation R is represented as the matrix R = (r_ik) _n×n in the case of r_ik =〈 (x_i, x_k) , μ (x_i, x_k) , υ (x_i, x_k) 〉 for all i, k = 1, 2, … n in the X = {x₁, x₂, … x_n) set. For any two intuitionistic fuzzy values (IFVs) r_ik = (μ_ik, υ_ik) and r_tl = (μ_tl, υ_tl) in R, intuitionistic fuzzy operations are describe as follows [60].

$r_{ik} \oplus r_{tl} = (μ_{ik} + μ_{tl} - μ_{ik} μ_{tl}, υ_{ik} υ_{tl})$ (3)

$r_{ik} \otimes r_{tl} = (μ_{ik} μ_{tl}, υ_{ik} + υ_{tl} - υ_{ik} υ_{tl})$ (4)

1. Determination of specialization degree

The linguistic terms of each specialist’s expertise in retail, e-commerce, and strategy were translated into intuitionistic fuzzy values. Then, by considering all expertise in these areas, intuitionistic fuzzy expertise value was obtained for each expert. Then, a single intuitionistic fuzzy expertise value was obtained by combining expertise in retail, e-commerce, and strategy fields for each expert.

The following calculations are used to merge the views of decision-makers [89].

$\begin{matrix} r_{ij} = {IFWA}_{λ} (r_{ij}^{(1)}, r_{ij}^{(2)}, \dots, r_{ij}^{(l)}) \\ = r_{ij}^{(1)} λ_{1} \oplus r_{ij}^{(2)} λ_{2} \oplus \dots \oplus r_{ij}^{(l)} λ_{l} \\ = [1 - \prod_{k = 1}^{l} {(1 - μ_{ij}^{(k)})}^{λ_{k}}, \prod_{k = 1}^{l} {(υ_{ij}^{(k)})}^{λ_{k}}, \prod_{k = 1}^{l} {(1 - μ_{ij}^{(k)})}^{λ_{k}} - \prod_{k = 1}^{l} {(υ_{ij}^{(k)})}^{λ_{k}}] \end{matrix}$ (5)

2. Determining the weights of experts’ opinions

Based on the determined intuitionistic fuzzy expertise values, the degree of the weight of each specialist’s opinion was determined.

The number of decision-makers is l, and their importance degree is different. The weight vector of the decision-makers is λ ={ λ₁, λ₂, …, λ_l }, λ_k ⩾ 0, k = 1, 2, …, l, and $\sum_{k = 1}^{l} λ_{k} = 1$ . Decision matrix of the k. decision-maker is $R^{k} = {(r_{ij}^{(k)})}_{m \times n}$ , and $r_{ij}^{(k)} = (μ_{ij}^{(k)}, υ_{ij}^{(k)}, π_{ij}^{(k)})$ is the intuitionistic fuzzy value of k. decision-maker for i. alternative and j. criteria. D_k = (μ_k, υ_k, π_k) is the intuitive fuzzy number that indicates the importance of the k. decision-maker. The weight of the k. decision-maker was calculated as follows [89].

$\begin{matrix} λ_{k} = \frac{(μ_{k} + π_{k} (\frac{μ_{k}}{μ_{k} + υ_{k}})}{\sum_{k = 1}^{l} (μ_{k} + π_{k} (\frac{μ_{k}}{μ_{k} + υ_{k}})} \\ λ_{k} ⩾ 0, k = 1, 2, \dots, l and \sum_{k = 1}^{l} λ_{k} = 1 \end{matrix}$ (6)

3. Acquisition of combined decision matrices of the criteria

Experts expressed that they used linguistic terms their comparison for criteria. After that, the linguistic expressions of each expert were translated into intuitionistic fuzzy values. Comparison matrices containing intuitionistic fuzzy values were combined with equation (5), considering their specialist weights. Combined matrices were obtained in which the main criteria and related sub-criteria were compared with each other.

4. Control of consistency in matrices

Perfect multiplicative consistent intuitionistic preference relations were created for comparison matrices. The consistency of the matrix was checked with the help of the perfect multiplicative consistent intuitionistic preference relations matrix. Consistent ones were developed for inconsistent matrices.

Xu proposed Algorithm 1 steps to obtain the $\bar{R} = {({\bar{r}}_{ik})}_{n \times n}$ matrix for testing consistency [60].

Algorithm 1

For k > i + l let ${\bar{r}}_{ik} = ({\bar{μ}}_{ik}, {\bar{υ}}_{ik})$ where;

${\bar{μ}}_{ik} = \frac{\sqrt[k - i - 1]{\prod_{t = i + 1}^{k - 1} μ_{ik} μ_{tk}}}{\sqrt[k - i - 1]{\prod_{t = i + 1}^{k - 1} μ_{ik} μ_{tk}} + \sqrt[k - i - 1]{\prod_{t = i + 1}^{k - 1} (1 - μ_{ik}) (1 - μ_{tk})}}$ (7)

${\bar{υ}}_{ik} = \frac{\sqrt[k - i - 1]{\prod_{t = i + 1}^{k - 1} υ_{ik} υ_{tk}}}{\sqrt[k - i - 1]{\prod_{t = i + 1}^{k - 1} υ_{ik} υ_{tk}} + \sqrt[k - i - 1]{\prod_{t = i + 1}^{k - 1} (1 - υ_{it}) (1 - υ_{tk})}}$ (8)

For k = i + 1 let ${\bar{r}}_{ik} = r_{ik}$

For k < i let ${\bar{r}}_{ik} = ({\bar{υ}}_{ik}, {\bar{μ}}_{ik})$ .

To control the consistency of the intuitionistic preference relationship R, the distance from $\bar{R}$ must be at an acceptable level. If,

$d (R, \bar{R}) < τ$ (9) it is consistent. This consistency is calculated using below [60].

$\begin{matrix} d (\bar{R}, R) = \frac{1}{2 (n - 1) (n - 2)} \sum_{i = 1}^{n} \sum_{k = 1}^{n} \\ (| {\bar{μ}}_{ik} - μ_{ik} | + | {\bar{υ}}_{ik} - υ_{ik} | + | {\bar{π}}_{ik} - π_{ik} |) \end{matrix}$ (10)

If the deviation between $d (\bar{R}, R)$ is too large, $\tilde{R} = ({\tilde{r}}_{ik})_{n \times n}$ ) is modified as follows. The value of σ, which we process as a control parameter, will be $\tilde{R} = R$ in the case of σ = 0, and $\tilde{R} = \bar{R}$ in the case of σ = 1 [60].

$\begin{matrix} {\tilde{μ}}_{ik} = \frac{(μ_{ik})^{1 - σ} ({\bar{μ}}_{ik})^{σ}}{(μ_{ik})^{1 - σ} ({\bar{μ}}_{ik})^{σ} + {(1 - μ_{ik})}^{1 - σ} {(1 - {\bar{μ}}_{ik})}^{σ}} \\ i, k = 1, 2 \dots, n \end{matrix}$ (11)

$\begin{array}{l} {\tilde{υ}}_{i k} = \frac{{(υ_{i k})}^{1 - σ} {({\bar{υ}}_{i k})}^{σ}}{{(υ_{i k})}^{1 - σ} {({\bar{υ}}_{i k})}^{σ} + {(1 - υ_{i k})}^{1 - σ} {(1 - {\bar{υ}}_{i k})}^{σ}} \\ i, k = 1, 2 \dots, n \end{array}$ (12)

Xu proposed algorithm 2 to modify calculations for situations where consistency cannot be achieved [60].

Algorithm 2

Take processing p as an iteration and accept its value as 1.

This case, $\bar{R}$ and R^(p) consistency is calculated as follows [60].

$\begin{matrix} d (\bar{R}, R^{(p)}) = \frac{1}{2 (n - 1) (n - 2)} \sum_{i = 1}^{n} \sum_{k = 1}^{n} \\ (| {\bar{μ}}_{ik} - μ_{ik}^{(p)} | + | {\bar{v}}_{ik} - v_{ik}^{(p)} | + | {\bar{π}}_{ik} - π_{ik}^{(p)} |) \end{matrix}$ (13)

If $d (\bar{R}, R^{(p)}) < τ$ is not provided, proceed to the following step. For small values of σ, R^(p) and ${\tilde{R}}^{(p)}$ will be close. Thus, while obtaining a consistent structure, there is not much loss from the original information of the decision-makers [60].

The following equations obtain the fused heuristic preference relation ${\tilde{R}}^{(p)} = {({\tilde{r}}_{ik}^{(p)})}_{n \times n}, ({\tilde{r}}_{ik}^{(p)} = ({\tilde{μ}}_{ik}^{(p)}, {\tilde{υ}}_{ik}^{(p)}))$ .

$\begin{matrix} {\tilde{μ}}_{ik}^{(p)} = \frac{(μ_{ik}^{(p)})^{1 - σ} ({\bar{μ}}_{ik})^{σ}}{(μ_{ik}^{(p)})^{1 - σ} ({\bar{μ}}_{ik})^{σ} + {(1 - μ_{ik}^{(p)})}^{1 - σ} {(1 - {\bar{μ}}_{ik})}^{σ}} \\ i, k = 1, 2 \dots, n \end{matrix}$ (14)

$\begin{matrix} {\tilde{υ}}_{ik}^{(p)} = \frac{(υ_{ik}^{(p)})^{1 - σ} ({\bar{υ}}_{ik})^{σ}}{(υ_{ik}^{(p)})^{1 - σ} ({\bar{υ}}_{ik})^{σ} + {(1 - υ_{ik}^{(p)})}^{1 - σ} {(1 - {\bar{υ}}_{ik})}^{σ}} \\ i, k = 1, 2 \dots, n \end{matrix}$ (15)

5. Calculation of weights of criteria

After the consistency stage, weights were calculated for the main and sub-criteria. The priority weights ω_i of the intuitionistic fuzzy values (IFV) for the intuitionistic preference relationship is calculated as follows [60].

$ω_{i} = (\frac{\sum_{k = 1}^{n} μ_{ik}}{\sum_{i = 1}^{n} \sum_{k = 1}^{n} (1 - υ_{ik})}, 1 - \frac{\sum_{k = 1}^{n} (1 - υ_{ik})}{\sum_{i = 1}^{n} \sum_{k = 1}^{n} μ_{ik}})$ (16)

The weights calculated for the main criteria were distributed to the sub-criteria with equation (4), and weights were used to evaluate the alternatives. At this stage, the ranking of the criteria was also obtained.

Let the intuitionistic fuzzy value α = (μ_α, υ_α, π_α), IFV rankings are calculated as follows. Small ρ (α) means the best one [60].

$ρ (α) = 0, 5 (1 + π_{α}) (1 - μ_{α})$ (17)

Step 2: IF TOPSIS

6. Intuitionistic fuzzy value calculations for alternatives

The linguistic terms that each specialist expressed according to the criteria for alternatives were translated into intuitionistic fuzzy values. Using Equation (5), criteria scores were combined for each alternative, considering the weight of each specialist’s views.

7. Generating the combined decision matrix

Multiplying the criterion weights with the criterion score to which each alternative belongs, the combined decision matrix is obtained (Equation 4).

8. Calculation of closeness coefficient

The positive and negative intuitionistic fuzzy ideal solution is calculated. The closeness coefficient is calculated for each alternative. Alternatives are ranked according to the closeness coefficient.

In the intuitionistic fuzzy TOPSIS method, the set of alternatives is A ={ A₁, A₂, …, A_m }, and the set of criteria is C ={ C₁, C₂, …, C_n }. Criterion weights is obtained with IFAHP. The next step is to determine the positive and negative intuitionistic fuzzy ideal solution. For benefit criterion set (J₁) and cost criterion set (J₂), positive intuitive fuzzy ideal solution (A^*), and negative intuitive fuzzy ideal solution (A^-) are calculated as follows [89].

$\begin{matrix} A^{*} & = (r_{1}^{' *}, r_{2}^{' *},, r_{n}^{' *}), r_{j}^{' *} \\ = (μ_{j}^{' *}, υ_{j}^{' *}, π_{j}^{' *}), j = 1, 2, \dots, n \end{matrix}$ (18)

$\begin{matrix} A^{-} & = (r_{1}^{' -}, r_{2}^{' -},, r_{n}^{' -}), r_{j}^{' -} \\ = (μ_{j}^{' -}, υ_{j}^{' -}, π_{j}^{' -}), j = 1, 2, \dots, n \end{matrix}$ (19)

$μ_{j}^{' *} = {(max_{i} {μ_{ij}^{'}} | j \in J_{1}), (min_{i} {μ_{ij}^{'}} | j \in J_{2})}$ (20)

$υ_{j}^{' *} = {(min_{i} {υ_{ij}^{'}} | j \in J_{1}), (max_{i} {υ_{ij}^{'}} | j \in J_{2})}$ (21)

$\begin{matrix} π_{j}^{' *} = {(1 - (max_{i} {μ_{ij}^{'}} - min_{i} {υ_{ij}^{'}} | j \in J_{1}), \\ (1 - min_{i} {μ_{ij}^{'}} - max_{i} {υ_{ij}^{'}} | j \in J_{2})} \end{matrix}$ (22)

$μ_{j}^{' -} = {(min_{i} {μ_{ij}^{'}} | j \in J_{1}), (max_{i} {μ_{ij}^{'}} | j \in J_{2})$ (23)

$υ_{j}^{' -} = {(max_{i} {υ_{ij}^{'}} | j \in J_{1}), (min_{i} {υ_{ij}^{'}} | j \in J_{2})}$ (24)

$\begin{matrix} π_{j}^{' -} = {(1 - min_{i} {μ_{ij}^{'}} - max_{i} {υ_{ij}^{'}} | j \in J_{1}), \\ (1 - max_{i} {μ_{ij}^{'}} - min_{i} {υ_{ij}^{'}} | j \in J_{2})} \end{matrix}$ (25)

The following equations are used to measure the distinction between alternatives, the positive ideal solution, and the negative ideal solution [89].

$\begin{matrix} S_{i}^{*} = \frac{1}{2} \sum_{j = 1}^{n} [| μ_{ij}^{'} - μ_{ij}^{' *}] | + | υ_{ij}^{'} - υ_{ij}^{' *} | + | π_{ij}^{'} - υ_{ij}^{' *} |, \\ i = 1, 2, \dots, m \end{matrix}$ (26)

$\begin{matrix} S_{i}^{-} = \frac{1}{2} \sum_{j = 1}^{n} [| μ_{ij}^{'} - μ_{ij}^{' -}] | + | υ_{ij}^{'} - υ_{ij}^{' -} | \\ + | π_{ij}^{'} - υ_{ij}^{' -} |, i = 1, 2, \dots, m \end{matrix}$ (27)

For each alternative A_i, the positive intuitionistic fuzzy ideal solution and the negative intuitionistic ideal solution closeness coefficient $C_{i}^{*}$ are calculated as follows. The size of the closeness coefficients sorts the alternatives [89].

$C_{i}^{*} = \frac{S_{i}^{-}}{S_{i}^{*} - S_{i}^{-}}, 0 ⩽ C_{i}^{*} ⩽ 1, i = 1, 2, \dots, m$ (28)

4 Application

Questionnaire answers of 9 participants were received for selection of competitive strategy with intuitionistic fuzzy AHP and TOPSIS method in retail e-commerce. Intuitionistic fuzzy numbers of linguistic terms used to determine the importance of decision-makers include in Table 4 [89]. Linguistic terms were modified according to the research subject.

Table 4
Linguistic terms for DMs’ importance degrees of criteria

Linguistic Terms Contraction Intuitionistic Fuzzy Value

Expert E (0.90,0.10)

Advanced A (0.75,0.20)

Proficient P (0.50,0.45)

Advanced Beginner AB (0.35,0.60)

Novice N (0.10,0.90)

Linguistic Terms	Contraction	Intuitionistic Fuzzy Value
Expert	E	(0.90,0.10)
Advanced	A	(0.75,0.20)
Proficient	P	(0.50,0.45)
Advanced Beginner	AB	(0.35,0.60)
Novice	N	(0.10,0.90)

Decision-makers evaluated their expertise in three headings in terms of retail, e-commerce, and strategy. In these three titles, the degree of importance is considered equal (1/3). Decision-makers indicate their expertise on three topics (Table 5).

Table 5

Linguistic terms for DMs’ expert degrees

Decision Maker	Retail	E-Commerce	Strategy
DM1	A	AB	P
DM2	AB	E	A
DM3	E	AB	A
DM4	P	E	A
DM5	A	E	E
DM6	P	E	P
DM7	P	P	AB
DM8	E	E	E
DM9	A	A	A

For each decision-maker, the expertise of the three titles was combined using Equation (5) (Table 6). The sample calculation for DM1 is below.

Table 6

Consolidated & importance level DMs’ expert degrees

Decision maker	IFV	λ _DMI
DM1	(0.57,0.38)	0.09
DM2	(0.75,0.23)	0.11
DM3	(0.75,0.23)	0.11
DM4	(0.77,0.21)	0.12
DM5	(0.86,0.13)	0.13
DM6	(0.71,0.27)	0.11
DM7	(0.45,0.50)	0.07
DM8	(0.90,0.10)	0.13
DM9	(0.75,0.20)	0.12

$\begin{matrix} w_{μ_{DM 1}} = & 1 - ((1 - 0.75)^{\frac{1}{3}} \times (1 - 0.35)^{\frac{1}{3}} \\ \times (1 - 0.5)^{\frac{1}{3}}) = 0.57 \end{matrix}$ $w_{υ_{DM 1}} = ((0.20)^{\frac{1}{3}} \times (0.60)^{\frac{1}{3}} \times (0.45)^{\frac{1}{3}}) = 0.38$

$\begin{matrix} w_{π_{DM 1}} = 1 - (1 - ((1 - 0.75)^{\frac{1}{3}} \times (1 - 0.35)^{\frac{1}{3}} \times (1 - 0.5)^{\frac{1}{3}}) \\ - ((0.20)^{\frac{1}{3}} \times (0.60)^{\frac{1}{3}} \times (0.45)^{\frac{1}{3}}) = 0.06 \end{matrix}$

After obtaining a single specialization degree for each decision-maker, the importance level of 9 participants is determined with the help of Equation (6) (Table 6). The importance of DM1 is calculated below as an example. $\begin{matrix} \begin{matrix} (μ_{k} + π_{k} (\frac{μ_{k}}{μ_{k} + υ_{k}}) \\ = (0.57 + 0.06 \times (0.57 \div (0.57 + 0.38))) = 0.60 \\ λ_{DM 1} = 0.60 \div (0.60 + 0.77 + 0.77 + 0.79 + 0.87 \\ + 0.72 + 0.48 + 0.90 + 0.79) = 0.09 \end{matrix} \end{matrix}$

The linguistic terms used in the evaluation of the criteria include in Table 7 [90]. Linguistic terms were modified according to the research subject.

Table 7

Linguistic terms for criteria comparison

Linguistic Terms	Contraction	IFV
Most Important	MI	(1.00,0.00)
Very Important	VI	(0.75,0.10)
Important	I	(0.60,0.25)
Little More Important	LMI	(0.50,0.40)
Equal	E	(0.50,0.50)
Little Less Important	LLI	(0.40,0.50)
Less Important	LI	(0.25,0.60)
Very Little Important	VLI	(0.10,0.75)
Unimportant	U	(0.00,0.90)

Table 8 shows the comparative significance degree that decision-makers have given to the criteria for the three main criteria, environment (M1), operation (M2), and customer (M3). For example, DM1 states the environmental criteria as LI (Less Important) according to operation criterion.

Table 8

Linguistic expressions of decision-makers’ comparisons for the main criteria

Decision maker	M1-M2	M1-M3	M2-M3
DM1	LI	LI	E
DM2	LI	LI	E
DM3	I	LI	LI
DM4	VI	E	VLI
DM5	VI	LI	LI
DM6	LMI	LMI	I
DM7	E	LLI	LLI
DM8	E	U	U
DM9	VI	I	I

Table 9 contains the linguistic comparison of decision-makers for the sub-criteria of the main criteria. For example, DM1 express its competitive environment as LMI (Little More Important) according to the legal environment.

Table 9

Linguistic expressions of decision-makers’ comparisons for sub-criteria

DM	Environment			Operation			Customer
C1-C2	C1-C3	C2-C3	C4-C5	C4-C6	C5-C6	C7-C8	C7-C9	C7-C10	C7-C11	C8-C9	C8-C10	C8-C11	C9-C10	C9-C11	C10-C11
DM1	LI	LMI	VI	E	LLI	LLI	VLI	VLI	VLI	LMI	VLI	LI	LMI	E	I	I
DM2	MI	I	E	VLI	VLI	LLI	VLI	LI	U	VLI	I	E	I	LI	E	E
DM3	I	E	I	I	E	E	I	E	LI	I	LI	LI	E	I	I	I
DM4	I	I	E	VI	LI	LI	VI	E	E	I	LI	E	I	VI	LMI	I
DM5	E	LLI	LLI	LMI	LLI	E	VI	LMI	E	E	E	LLI	LLI	LMI	LMI	LMI
DM6	I	LLI	LLI	I	LMI	E	LLI	E	LI	VLI	E	VLI	LI	E	LMI	LMI
DM7	LLI	I	VI	LMI	LLI	LLI	I	E	LLI	VI	LMI	E	VI	E	VI	VI
DM8	E	E	E	E	E	E	LMI	LLI	LI	E	LI	LI	E	LLI	E	E
DM9	MI	I	LMI	I	LMI	I	LI	LI	LLI	E	I	E	LMI	E	LLI	VLI

The consolidated decision matrices, which include the importance degree of decision-makers for the main criteria and sub-criteria, were calculated with Equation (5). The consolidated preference relationship matrix R_M of the main criteria is specified in Table 10.

Table 10

Consolidated IFVs for the main criteria

R _M	M1	M2	M3
M1	(0.50,0.50)	(0.59,0.26)	(0.35,0.53)
M2	(0.37,0.51)	(0.50,0.50)	(0.37,0.51)
M3	(1.00,0.00)	(1.00,0.00)	(0.50,0.50)

In the consolidated preference relationship matrix, r_{M
₁₂} is calculates as follows. $\begin{matrix} \begin{matrix} r_{M_{12}} = (μ_{M_{12}}, υ_{M_{12}}) \\ μ_{M_{12}} = 1 - (1 - 0.25)^{0.09} \times (1 - 0.25)^{0.11} \\ \times (1 - 0.60)^{0.11} \times (1 - 0.75)^{0.12} \times (1 - 0.75)^{0.13} \\ \times (1 - 0.50)^{0.11} \times (1 - 0.50)^{0.07} \times (1 - 0.50)^{0.13} \\ \times (1 - 0.75)^{0.12} = 0.59 \\ υ_{M_{12}} = (1 - 0.60)^{0.09} \times (1 - 0.60)^{0.11} \\ \times (1 - 0.25)^{0.11} \times (1 - 0.10)^{0.12} \times (1 - 0.10)^{0.13} \\ \times (1 - 0.40)^{0.11} \times (1 - 0.50)^{0.07} \\ \times (1 - 0.50)^{0.13} \times (1 - 0.10)^{0.12} = 0.26 \end{matrix} \end{matrix}$

Table 11: Consolidated IFVs for environmental sub-criteria shows the consolidated preference relation matrix R_E belonging to the sub criteria of the environment.

Table 11

Consolidated IFVs for environmental sub-criteria

R_E	C1	C2	C3
C1	(0.50,0.50)	(1.00,0.00)	(0.52,0.37)
C2	(0.34,0.56)	(0.50,0.50)	(0.54,0.35)
C3	(0.40,0.51)	(0.41,0.52)	(0.50,0.50)

Table 12 shows the consolidated preference relation matrix R_O belonging to the sub-criteria of the operation criterion.

Table 12

Consolidated IFVs for operational sub-criteria

R_O	C4	C5	C6
C4	(0.50,0.50)	(0.54,0.33)	(0.41,0.51)
C5	(0.41,0.46)	(0.50,0.50)	(0.46,0.47)
C6	(0.53,0.36)	(0.49,0.44)	(0.50,0.50)

The consolidated preference relationship matrix R_C, belonging to the sub-criteria of customer main criterion is specified in Table 13.

Table 13

Consolidated IFVs for customer sub-criteria

R_C	C7	C8	C9	C10	C11
C7	(0.50,0.50)	(0.51,0.32)	(0.41,0.53)	(0.32,0.59)	(0.49,0.41)
C8	(0.46,0.37)	(0.50,0.50)	(0.42,0.46)	(0.37,0.56)	(0.52,0.37)
C9	(0.54,0.36)	(0.52,0.35)	(0.50,0.50)	(0.52,0.38)	(0.54,0.36)
C10	(1.00,0.00)	(0.57,0.32)	(0.44,0.48)	(0.50,0.50)	(0.53,0.35)
C11	(0.50,0.38)	(0.42,0.48)	(0.40,0.52)	(0.43,0.45)	(0.50,0.50)

Algorithm 1 is applied to test the consistency of intuitionistic preference relationships. The perfect multiplication consistent intuitionistic preference relationship matrix ${\bar{R}}_{M}$ is calculated for the main criteria denote in Table 14.

Table 14

The multiplicative consistent intuitionistic preference relation for main criteria

${\bar{R}}_{M}$	M1	M2	M3
M1	(0.50,0.50)	(0.59,0.26)	(0.46,0.27)
M2	(0.26,0.59)	(0.50,0.50)	(0.37,0.51)
M3	(0.27,0.46)	(0.51,0.37)	(0.50,0.50)

The perfect multiplicative consistent intuitionistic preference relationship matrix of ${\bar{R}}_{E}$ , calculated for the sub-criteria of the environment main criterion, is given in Table 15.

Table 15

The multiplicative consistent intuitionistic preference relation for environmental sub-criteria

${\bar{R}}_{E}$	C1	C2	C3
C1	(0.50,0.50)	(1.00,0.00)	(1.00,0.00)
C2	(0.00,1.00)	(0.50,0.50)	(0.54,0.35)
C3	(0.00,1.00)	(0.35,0.54)	(0.50,0.50)

The perfect multiplicative consistent intuitionistic preference relationship matrix ${\bar{R}}_{O}$ , calculated for the sub-criteria of the operation main criterion, is given in Table 16.

Table 16

The multiplicative consistent intuitionistic preference relation for operational sub-criteria

${\bar{R}}_{O}$	C4	C5	C6
C4	(0.50,0.50)	(0.54,0.33)	(0.51,0.30)
C5	(0.33,0.54)	(0.50,0.50)	(0.46,0.47)
C6	(0.30,0.51)	(0.47,0.46)	(0.50,0.50)

The perfect multiplicative consistent intuitionistic preference relationship matrix ${\bar{R}}_{C}$ , calculated for the sub-criteria of the customer main criterion, is given in Table 17.

Table 17

The multiplicative consistent intuitionistic preference relation for operational sub-criteria

${\bar{R}}_{C}$	C7	C8	C9	C10	C11
C7	(0.50,0.50)	(0.51,0.32)	(0.43,0.29)	(0.40,0.39)	(0.51,0.26)
C8	(0.32,0.51)	(0.50,0.50)	(0.42,0.46)	(0.44,0.35)	(0.43,0.37)
C9	(0.29,0.43)	(0.29,0.42)	(0.50,0.50)	(0.52,0.38)	(0.54,0.02)
C10	(0.39,0.40)	(0.35,0.44)	(0.38,0.52)	(0.50,0.50)	(0.53,0.35)
C11	(0.26,0.51)	(0.37,0.43)	(0.02,0.54)	(0.35,0.53)	(0.50,0.50)

For example, ${\bar{r}}_{C_{710}} = ({\bar{μ}}_{C_{710}}, {\bar{υ}}_{C_{710}})$ is calculated with Equations (7)–(8) as follows. $\begin{matrix} \begin{matrix} {\bar{μ}}_{C_{710}} = \frac{\sqrt[4 - 1 - 1]{0.51 \times 0.37 \times 0.41 \times 0.52}}{0.20 + \sqrt[4 - 1 - 1]{(1 - 0.51) \times (1 - 0.37) \times (1 - 0.41) \times (1 - 0.52)}} \\ {\bar{μ}}_{C_{710}} = 0.40 \end{matrix} \end{matrix}$ $\begin{matrix} \begin{matrix} {\bar{υ}}_{C_{710}} = \frac{\sqrt[4 - 1 - 1]{0.32 \times 0.56 \times 0.53 \times 0.38}}{0.19 + \sqrt[4 - 1 - 1]{(1 - 0.32) \times (1 - 0.56) \times (1 - 0.53) \times (1 - 0.38)}} \\ {\bar{υ}}_{C_{710}} = 0.39 \end{matrix} \end{matrix}$

To check the consistency, the distance of the intuitionistic preference relationship R with $\bar{R}$ is controlled by Equation (10). If $d (\bar{R}, R) < τ$ and τ = 0.1, the outcome is R [60]. The values calculated for the main and sub-criteria are given in Table 18.

Table 18

$d (\bar{R}, R)$ For Main and Sub-criteria

Main/Sub -Criteria	$d (\bar{R}, R)$	Main/Sub -Criteria	$d (\bar{R}, R)$
M1	0.13	C5	0.04
M2	0.05	C6	0.12
M3	0.61	C7	0.04
C1	0.24	C8	0.03
C2	0.22	C9	0.06
C3	0.28	C10	0.07
C4	0.10	C11	0.07
M1	0.13	C5	0.04
M2	0.05	C6	0.12

The calculation for the main criterion M1 is given below. $\begin{matrix} \begin{matrix} d ({\bar{R}}_{M 1}, R_{M 1}) = \frac{1}{2 (3 - 1) (3 - 2)} (| 0.50 - 0.50 | \\ + | 0.59 - 0.59 | + | 0.35 - 0.46 | + | 0.50 - 0.50 | \\ + | 0.26 - 0.26 | + | 0.53 - 0.27 | + | 0.00 - 0.00 | \\ + | 0.15 - 0.15 | + | 0.12 - 0.27 |) = 0.13 \end{matrix} \end{matrix}$

According to calculations, some criteria could not provide consistency. With the help of Equations (14) and (15), ${\tilde{R}}^{(p)}$ is calculated by using Algorithm 2 (σ = 0.8). The fused intuitionistic preference relation matrix ${\tilde{R}}_{M}$ calculated for the main criteria is given in Table 19.

Table 19

The fused multiplicative consistent intuitionistic preference relation for main criteria

${\tilde{R}}_{M}$	M1	M2	M3
M1	(0.50,0.50)	(0.59,0.26)	(0.44,0.31)
M2	(0.26,0.59)	(0.50,0.50)	(0.37,0.51)
M3	(0.31,0.44)	(0.51,0.37)	(0.50,0.50)

The fused intuitionistic preference relation matrix ${\tilde{R}}_{E}$ calculated for the sub-criteria of the environment is given in Table 20.

Table 20

The fused multiplicative consistent intuitionistic preference relation for environmental sub-criteria

${\tilde{R}}_{M}$	C1	C2	C3
C1	(0.50,0.50)	(1.00,0.00)	(1.00,0.00)
C2	(0.00,1.00)	(0.50,0.50)	(0.54,0.35)
C3	(0.00,1.00)	(0.35,0.54)	(0.50,0.50)

The fused intuitionistic preference relation matrix ${\tilde{R}}_{O}$ calculated for the sub-criteria of the operation is given in Table 21.

Table 21

The fused multiplicative consistent intuitionistic preference relation for operational sub-criteria multiplicative

${\tilde{R}}_{O}$	C4	C5	C6
C4	(0.50,0.50)	(0.54,0.33)	(0.49,0.34)
C5	(0.33,0.54)	(0.50,0.50)	(0.46,0.47)
C6	(0.34,0.49)	(0.47,0.46)	(0.50,0.50)

The consistency of the fused intuitionistic preference relationship matrices with the perfect multiplication-consistent intuitionistic preference relationship matrices is checked with Equation (13). Calculated values are given in Table 22.

Table 22

$d (\bar{R}, \tilde{R})$ For Main and Sub-criteria

Main/Sub -Criteria	$d (\bar{R}, \tilde{R})$	Main/Sub -Criteria	$d (\bar{R}, \tilde{R})$
M1	0.02	C3	0.00
M2	0.00	C4	0.02
M3	0.02	C5	0.00
C1	0.00	C6	0.02
C2	0.00

After ensuring the consistency, the priority weight of the main and sub-criteria is calculated with the help of Equation (16). The priority weights calculated for the criteria are given in Table 23. For example, the priority weight of the M1 criterion is calculated as follows.

Table 23

ω_i For Main and Sub-criteria

Main/Sub -Criteria	ω _i	Main/Sub -Criteria	ω _i
M1	(0.30,0.52)	C5	(0.26,0.64)
M2	(0.23,0.65)	C6	(0.27,0.62)
M3	(0.26,0.58)	C7	(0.16,0.68)
C1	(0.54,0.43)	C8	(0.14,0.72)
C2	(0.23,0.74)	C9	(0.15,0.68)
C3	(0.18,0.78)	C10	(0.15,0.70)
C4	(0.31,0.56)	C11	(0.10,0.76)

$\begin{matrix} \begin{matrix} ω_{μ M 1} = \\ \frac{(0.50 + 0.59 + 0.44)}{(1 - 0.50) + (1 - 0.26) + (1 - 0.31) + (1 - 0.59) + (1 - 0.50) + (1 - 0.51) + (1 - 0.44) + (1 - 0.37) + (1 - 0.50)} \\ = 0.30 \\ ω_{υ M 1} = 1 \frac{((1 - 0.50) + (1 - 0.26) + (1 - 0.31))}{(0.50 + 0.59 + 0.44 + 0.26 + 0.50 + 0.37 + 0.31 + 0.51 + 0.50)} = 0.52 \end{matrix} \end{matrix}$

The main criteria weights distribute to the sub-criteria with the help of Equation (4). The sub-criteria weights, whose main criterion weights are distributed, are given in Table 24. The calculation for the C1 criterion is given below as an example.

Table 24

Sub-criterion weights with distributed main criteria weights

Sub -Criteria	w_ij	Sub -Criteria	w_ij
C1	(0.17,0.72)	C7	(0.04,0.87)
C2	(0.07,0.87)	C8	(0.04,0.88)
C3	(0.06,0.89)	C9	(0.04,0.87)
C4	(0.07,0.84)	C10	(0.04,0.88)
C5	(0.06,0.87)	C11	(0.03,0.90)
C6	(0.06,0.87)

$\begin{matrix} \begin{matrix} w_{μ_{C 1}} = 0.34 \times 0.54 = 0.17 \\ w_{υ_{C 1}} = (0.52 + 0.43) - 0.52 \times 0.43 = 0.72 \\ w_{π_{C 1}} = 1 - 0.17 - 0.72 = 0.11 \end{matrix} \end{matrix}$

The ranking of the criteria was made with the help of equation (17). Values calculated accordingly are given in Table 25. The calculation for C1 is given below. The most important criteria stand out as the competitive environment, demographic, cultural and economic environment, legal environment and converting resources to competitive advantage.

Table 25

Ranking Criteria

C	IFVs	p (a)	Rank
C1	(0.17,0.72)	0.464	1
C2	(0.07,0.87)	0.493	2
C3	(0.06,0.89)	0.495	3
C4	(0.07,0.84)	0.504	6
C5	(0.06,0.87)	0.502	4
C6	(0.06,0.87)	0.503	5
C7	(0.04,0.87)	0.523	9
C8	(0.04,0.88)	0.519	8
C9	(0.04,0.87)	0.527	11
C10	(0.04,0.88)	0.518	7
C11	(0.03,0.90)	0.524	10

$\begin{matrix} p (α) = 0.5 \times (1 + 0.11) \times (1 - 0.17) = 0.464 \end{matrix}$

The linguistic terms used in the evaluation of alternatives are shown in Table 26 [89]. Linguistic terms were modified according to the research subject.

Table 26

Linguistic terms for DMs’ importance degrees of alternative

Linguistic Terms	Contraction	IFV
Most Successful	MS	(0.75,0.10)
Very Successful	VS	(0.60,0.25)
Successful	S	(0.50,0.50)
Little Successful	LS	(0.25,0.60)
Unsuccessful	U	(0.10,0.75)

The decision-makers’ evaluation of the cost leadership alternative according to the criteria is given in Table 27.

Table 27

Linguistic expressions of decision-makers’ comparisons for cost leadership alternative strategy

DM	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10
DM1	MS	VS	S	LS	MS	VS	S	VS	MS	S
DM2	MS	LS	LS	VS	MS	MS	LS	LS	VS	VS
DM3	MS	VS	LS	S	MS	VS	LS	LS	VS	LS
DM4	VS	S	LS	VS	MS	VS	S	VS	VS	S
DM5	MS	MS	MS	MS	MS	MS	S	MS	MS	S
DM6	MS	VS	VS	VS	VS	VS	S	VS	MS	VS
DM7	MS	VS	S	VS	VS	VS	VS	S	MS	VS
DM8	S	S	S	VS	VS	S	U	U	LS	U
DM9	VS	S	LS	S	S	VS	LS	VS	S	U

The decision-makers’ evaluation of the differentiation alternative according to the criteria is given in Table 28.

Table 28

Linguistic expressions of decision-makers’ comparisons for differentiation alternative strategy

DM	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10
DM1	MS	MS	S	MS	VS	MS	LS	MS	MS	MS
DM2	MS	S	U	S	MS	VS	MS	MS	VS	VS
DM3	MS	VS	LS	LS	S	VS	LS	LS	VS	VS
DM4	MS	MS	LS	VS	MS	MS	S	MS	VS	MS
DM5	MS	VS	VS	MS	MS	MS	VS	VS	VS	MS
DM6	VS	VS	VS	VS	VS	VS	VS	VS	VS	VS
DM7	VS	S	LS	S	S	VS	S	VS	VS	S
DM8	VS	VS	LS	S	S	VS	MS	MS	MS	MS
DM9	VS	VS	LS	S	S	VS	U	S	S	LS

The decision-makers’ evaluation of the focusing alternative according to the criteria is given in Table 29.

Table 29

Linguistic expressions of decision makers’ comparisons for focus alternative strategy

DM	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10
DM1	VS	MS	S	MS	MS	MS	LS	MS	MS	VS
DM2	LS	VS	S	S	S	VS	VS	S	VS	S
DM3	MS	VS	LS	S	VS	VS	LS	LS	VS	MS
DM4	MS	VS	LS	MS	S	MS	S	MS	S	MS
DM5	MS	VS	S	VS	VS	VS	S	MS	MS	MS
DM6	VS	VS	VS	VS	VS	VS	VS	VS	VS	VS
DM7	MS	S	S	S	LS	VS	S	VS	VS	VS
DM8	MS	S	U	S	S	S	VS	VS	VS	VS
DM9	VS	VS	LS	LS	S	VS	LS	LS	LS	U

The opinions of the decision-makers for each criterion are combined, considering the importance of the decision-makers. The views are combined with equation (5). Consolidated values are given in Table 30.

Table 30

Criterion weights and consolidated IFVs for alternatives

C	w _ij	A1	A2	A3
C1	(0.17,0.72)	(0.69,0.15)	(0.69,0.15)	(0.67,0.16)
C2	(0.07,0.87)	(0.56,0.32)	(0.62,0.24)	(0.60,0.27)
C3	(0.06,0.89)	(0.46,0.41)	(0.36,0.49)	(0.39,0.52)
C4	(0.07,0.84)	(0.58,0.28)	(0.57,0.31)	(0.57,0.31)
C5	(0.06,0.87)	(0.69,0.16)	(0.63,0.24)	(0.55,0.34)
C6	(0.06,0.87)	(0.63,0.22)	(0.66,0.18)	(0.63,0,23)
C7	(0.04,0.87)	(0.39,0.54)	(0.54,0.31)	(0.47,0.41)
C8	(0.04,0.88)	(0.51,0.33)	(0.64,0.20)	(0.59,0.24)
C9	(0.04,0.87)	(0.63,0.21)	(0.63,0.22)	(0.60,0.25)
C10	(0.04,0.88)	(0.43,0.46)	(0.65,0.19)	(0.62,0.22)
C11	(0.03,0.90)	(0.46,0.37)	(0.58,0.31)	(0.61,0.27)

Then weights were distributed to alternatives with the help of Equation (4). The intuitionistic fuzzy values after distribution are given in Table 31.

Table 31

Consolidated IFVs with distributed weights for alternative strategies

C	A1	A2	A3
C1	(0.11,0.77)	(0.11,0.77)	(0.11,0.77)
C2	(0.04,0.91)	(0.04,0.90)	(0.04,0.91)
C3	(0.03,0.94)	(0.02,0.95)	(0.02,0.95
C4	(0.04,0.89)	(0.04,0.89)	(0.04,0.89)
C5	(0.04,0.89)	(0.04,0.90)	(0.03,0.92)
C6	(0.04,0.90)	(0.04,0.89)	(0.04,0.90)
C7	(0.02,0.94)	(0.02,0.91)	(0.02,0.92)
C8	(0.02,0.92)	(0.02,0.91)	(0.02,0.91)
C9	(0.02,0.89)	(0.02,0.89)	(0.02,0.90)
C10	(0.02,0.94)	(0.02,0.91)	(0.02,0.91)
C11	(0.01,0.93)	(0.02,0.93)	(0.02,0.92)

All criteria are benefit criteria. The A^* positive intuitionistic fuzzy ideal solution and A^- the negative intuitionistic fuzzy ideal solution are calculated using equations between (18) to (25). All calculated values are included in Table 32. As an example, calculations for C3 are as follows.

Table 32

Positive intuitionistic fuzzy ideal solutions and negative intuitionistic ideal solutions

C	A ^*	A ^-
C1	(0.11,0.77)	(0.11,0.77)
C2	(0.04,0.90)	(0.04,0.91)
C3	(0.03,0.94)	(0.02,0.95)
C4	(0.04,0.89)	(0.04,0.89)
C5	(0.04,0.89)	(0.03,0.92)
C6	(0.04,0.89)	(0.04,0.90)
C7	(0.02,0.91)	(0.02,0.94)
C8	(0.02,0.91)	(0.02,0.92)
C9	(0.02,0.89)	(0.02,0.90)
C10	(0.02,0.91)	(0.02,0.94)
C11	(0.02,0.92)	(0.01,0.93)

$\begin{matrix} \begin{matrix} μ_{C 3}^{' *} = max (0.03, 0.02, 0.02) = 0.03 \\ υ_{C 3}^{' *} = min (0.94, 0.95, 0.95) = 0.94 \\ μ_{C 3}^{' -} = min (0.03, 0.02, 0.02) = 0.02 \\ υ_{C 3}^{' -} = max (0.94, 0.95, 0.95) = 0.95 \end{matrix} \end{matrix}$

Equations (26) and (27) were used to measure the difference between the positive ideal solution and the negative ideal solution, and Equation (28) was used for the closeness coefficients. The Si^* and Ci^* for A1 is calculated as follows. $\begin{matrix} \begin{matrix} {Si}^{*} = \frac{1}{2} (| 0.11 - 0.11 | + | 0.04 - 0.04 | \\ + | 0.03 - 0.03 | + | 0.04 - 0.04 | + | 0.04 - 0.04 | \\ + | 0.04 - 0.04 | + | 0.02 - 0.02 | + | 0.02 - 0.02 | \\ + | 0.02 - 0.02 | + | 0.02 - 0.02 | + | 0.01 - 0.02 | \\ + | 0.77 - 0.77 | + | 0.91 - 0.90 | + | 0.94 - 0.94 | \\ + | 0.89 - 0.89 | + | 0.89 - 0.89 | + | 0.90 - 0.89 | \\ + | 0.94 - 0.91 | + | 0.92 - 0.91 | + | 0.89 - 0.89 | \\ + | 0.94 - 0.91 | + | 0.93 - 0.92 | + | 0.12 - 0.12 | \\ + | 0.05 - 0.05 | + | 0.04 - 0.04 | + | 0.07 - 0.07 | \\ + | 0.07 - 0.07 | + | 0.06 - 0.07 | + | 0.05 - 0.07 | \\ + | 0.06 - 0.07 | + | 0.08 - 0.08 | + | 0.05 - 0.07 | \\ + | 0.05 - 0.06 |) = 0.10 \end{matrix} \end{matrix}$ $\begin{matrix} {Ci}^{*} = \frac{(0.05)}{(0.05 + 0.10)} = 0.32 \end{matrix}$

The Si^*, Si^- and Ci^* values calculated for the alternatives are given in Table 33. The size of the closeness coefficients sorts the alternatives. The alternative with the highest closeness coefficient has the highest score, while the alternative with the lowest closeness coefficient has the lowest score [89].

Table 33

Si^*, Si^- and Ci^* For alternative strategies

A A	Si^* (Positive ideal)	Si^- (Negative ideal)	Ci^* (Closeness coefficient)
A1	0.10	0.05	0.32
A2	0.03	0.12	0.81
A3	0.08	0.07	0.47

In addition, the existing data were evaluated by fuzzy AHP [91 –93] and fuzzy TOPSIS method [94, 95] and similar results were obtained (Table 34).

Table 34

Results of different fuzzy decision-making methods

A	Intuitionistic Fuzzy AHP &TOPSIS	Rank	Fuzzy AHP&TOPSIS	Rank
A1	0.32	3	0.051	3
A2	0.81	1	0.057	1
A3	0.47	2	0.053	2

Although there is a numerical difference between the methods in which cost leadership (A1), differentiation (A2), and focusing (A3) strategies are evaluated, the order is the same in both.

The power of the proposed methodology with respect to ordinary fuzzy methodology is its consideration of decision makers’ hesitancies. Even the ranking results of both methodologies are same, the proposed methodology’s outcome involves the hesitancy information in assigning the membership and nonmembership degrees. Alternative A2 is preferred by the proposed methodology with a much stronger closeness coefficient than ordinary fuzzy methodology’s. This is because of the additional information in the proposed methodology.

5 Conclusion

In this study, competition strategy selection was carried out with the help of intuitionistic fuzzy AHP and TOPSIS methods in retail e-commerce. It is crucial to apply the intuitionistic fuzzy decision-making method on strategy selection that includes personal judgments. With this method, experts’ agreements, disagreements, and hesitations can be evaluated in mathematical calculation.

The main criteria, sub-criteria, and alternatives that constitute the hierarchical structure of decision making were determined by taking the opinions of the literature and experts. Based on the expert opinions, the importance of the criteria was calculated according to each other in the IFAHP section, and the necessary arrangements were made by checking the consistency. In the IF TOPSIS step, all experts’ opinion of the criteria was combined, and a single intuitionistic fuzzy value was obtained for each criterion. Then, weights obtained in the IFAHP step were distributed to the criteria. With the values obtained, closeness to the positive ideal and distance to the negative ideal, and closeness coefficients were determined.

As a result of the study, two main outcomes were obtained. The first is the competition strategy based on the opinion of experts for retail e-commerce, and the second is the criteria rankings that can be interpreted as the focus of the companies. At the end of the calculations, it is seen that the participants firstly preferred differentiation as a competitive strategy. On the other hand, the alternative with the lowest score is cost leadership. It has also been determined that decision-makers see the competitive environment, demographic, cultural and economic environment, legal environment and convert resources to competitive advantage as the most important criteria.

The limitations of this study are the number of participants and the characteristics of the country of study. The research does not focus on a specific sector and area in retail e-commerce. However, the study is important in terms of both using the common mind in determining the strategy and giving an idea despite all its limitations.

References

emarketer, “Global Ecommerce 2019,” emarketer, 2020. [Online]. Available: https://www.emarketer.com/content/global-ecommerce-2019. [Accessed 28 6 2020].

TUBİSAD, “TÜBİSAD E-Commerce in Turkey Market Definition and Sizing Reports,” 2020. [Online]. Available: http://www.tubisad.org.tr/en/images/pdf/tubisad_2019_ecommerce.pdf. [Accessed 28 6 2020].

Clement

, Digital Market Outlook: retail e-commerce sales CAGR in selected markets 2020–2024, 15 July 2020. [Online]. Available: https://www.statista.com/statistics/220177/b2c-e-commerce-sales-cagr-forecast-for-selected-countries/. [Accessed 24 July 2020].

The Union of Chambers and Commodity Exchanges of Turkey, “Company Establishment and Liquidation Statistics,” (2020). [Online]. Available: https://www.tobb.org.tr/BilgiErisimMudurlugu/Sayfalar/KurulanKapananSirketistatistikleri.php. [Accessed 28 06 2020].

Zadeh

L.A.

, Fuzzy Sets, Information and Control 8 (1965), 338–353.

Liu

H.-W.

and Wang

G.-J.

, Multi-criteria decision-making methods based on intuitionistic fuzzy sets, European Journal of Operational Research 179 (2007), 220–233.

Samson

H.E.

, The Nature and Characteristics of Middle Management in Retail Department Stores, (1969). [Online]. Available: https://files.eric.ed.gov/fulltext/ED051394.pdf. [Accessed 6 10 2019].

Kotler

, Armstrong

and Opresnik

M.O.

, Retailing, in Princeples of Marketing, 17. ed., Harlow, UK, Pearson, (2018), 392–400.

Perreault

W.D.

, Cannon

J.P.

and McCarthy

E.J.

, Retailers, Wholesalers, and Their Strategy Planning, in Essentials of Marketing, 13th ed., New York, NY, USA: McGraw-Hill, (2012), 292–219.

10.

Zentes

, Morschett

and Schramm-Klein

, Retail Function, in Strategic Retail Management, 3th ed., Wiesbaden, Germany: Springer Gabler, (2017), 3–24.

11.

Mucan

, The Effect Of Consumers’ Store Experiences On Purchase Intention and An Application In Apparel Retailing, Ph.D. dissertation, Dept. of Business Administration, Dokuz Eylül University, Izmir, Turkey, (2014).

12.

Gülşen

İ.

, Innovation and Business Performance in Retailing, Ph.D. dissertations, Dept. Business Admistration, Afyon Kocatepe Univercity, Afyonkarahisar, Turkey, (2018).

13.

Turkish Statistical Insitute, “Metadata in Retail Sales Indices,” [Online]. Available: http://www.tuik.gov.tr/PreTablo.do?alt_id=1098. [Accessed 6 10 2019].

14.

Deloitte, “Global Power Of Retailing 2019,” (2019). [Online]. Available: https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Consumer-Business/cons-global-powers-retailing-2019.pdf, Accessed on: Oct. 3, 2019.

15.

KPMG, “Perakende Sektörel Bakiş,” 2019. [Online]. Available: https://assets.kpmg/content/dam/kpmg/tr/pdf/2019/03/sektorel-bakis-2019-perakende.pdf, Accessed on: Oct. 3, 2019.

16.

KPMG, “Fast Moving Consumer Goods,” 2016. [Online]. Available: https://assets.kpmg/content/dam/kpmg/br/pdf/2016/09/fast-moving-consumer-goods.pdf., Accessed on: Oct. 6, 2019.

17.

Indian Brand Equity Foundation, “Fast Moving Customer Goods,” Feb. 2018. [Online]. Available: https://www.ibef.org/download/FMCG-February-2018.pdf., Accessed on: Oct.6,2019.

18.

n11, “n11,” [Online]. Available: https://www.n11.com/ Accessed on: Oct. 6, 2019.

19.

hepsiburada, “Hepsiburada,” [Online]. Available: https://www.hepsiburada.com/, Accessed on: Oct. 6, 2019.

20.

amazon, “Amazon,” [Online]. Available: https://www.amazon.com.tr/, Accessed on: Oct. 6, (2019).

21.

gittigidiyor, “Gittigidiyor,” [Online]. Available: https://www.gittigidiyor.com/, Accessed on: Oct. 6, (2019).

22.

Beck

and Rygl

, Categorization of Multiple Channel Retailing in Multi-Cross-, and Omni-Channel Retailing for Retailers and Retailing, Journal of Retailing and Consumer Services 27 170–178. (2015), DOI: 10.1016/j.jretconser.2015.08.001

23.

Verhoefa

P.C.

, Kannanb

and Inman

J.J.

, From Multi-Channel Retailing to Omni-Channel Retailing Introduction to the Special Issue on Multi-Channel Retailing, Journal of Retailing 91(2) (2015), 174–181. DOI: 10.1016/j.jretai.2015.02.005

24.

Graf von Arnim

, Approaches for Steering Multichannel Retail Companies, in Performance Management in Retail and the Consumer Goods Industry, Cham, Switzerland, Springer, (2019), 69–84. DOI: 10.1007/978-3-030-12730-5_5

25.

Berman

, Evans

J.R.

and Chatterjee

, Operations Management: Financial Dimensions, in Retail Management A Strategic Approach, 13th Global ed., Vivar,Malaysia; Pearson, (2018), 315–331.

26.

Lewis

and Thomas

, The Linkage Between Strategy, Strategic Groups, and Performance in the U.K. Retail Grocery Industry, Strategic Management Journal 11(5) (1990), 385–397. https://www-jstor-org-443.web.bisu.edu.cn/stable/2486631.

27.

Gökalp

, Perakende Sektöründe Performans Ölçümü: Türkiye Üzerine Bir Model Denemesi, Ph.D. dissertation, Sosyal Bilimler Enstitüsü, Ege Üniversitesi, İzmir, Turkey, (2009).

28.

Berman

, Evans

J.R.

and Chatterjee

, An Introduction To Retailing,” in Retail Management A Strategic Approach, 13th Global ed., Vivar, Malaysia: Pearson, (2018), 22–43.

29.

Anand

and Grover

, Measuring retail supply chain performance Theoretical model using key performance indicators (KPIs), Benchmarking: An International Journal 22(1) (2015), 135–166. DOI: 10.1108/BIJ-05-2012-0034

30.

Barua

and Yin

, An Empirical Investigation of Net-Enabled Business Value, MIS Quarterly 28(40) (2004), 585–620.

31.

Rai

, Patnayakuni

and Seth

, Firm Performance Impacts of Digitally Enabled Supply Chain Integration Capabilities, MIS Quarterly 30(2) (2006), 225–246.

32.

Duana

, B.G. and Whinston

A.B.

, The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry, Journal of Retailing 84(2) 233–242 (2008), DOI: 10.1016/j.jretai.2008.04.005

33.

Cox

A.D.

, Cox

and Anderson

R.D.

, Reassessing the pleasures of store shopping,”, Journal of Business Research 58(3) (2005), 250–259. DOI: 10.1016/S0148-2963(03)00160-7

34.

Laudon

K.C.

and Travel

C.G.

, Introduction to E-Commerce, in E-Commerce 2018, 14th Global ed., Harlow, England: Pearson, (2018), 42–93.

35.

Turban

, Whiteside

, King

and Outland

, Owerview of Electronic Commerce and Social Commerce, in Introduction to Electronic Commerce and Social Commerce, 4th ed., Cham, Switzerland: Springer, (2017), 3–34.

36.

World Trade Organization, “Electronic Commerce,” Sep. 1998. [Online]. Available: https://www.wto.org/english/tratop_e/ecom_e/ecom_e.htm, Apr. 16, (2018).

37.

Huseynov

, Determining Online Consumer Typologies and Their Shopping Behaivors in B2C E-Commerce Platforms, Ph.D. dissertation, The Graduate School of Informatics, The Middle East Technical University, Ankara, Turkey, (2016).

38.

Turban

, Whiteside

, King

and Outland

, Retailing in Electronic Commerce: Products and Services, in Introduction to Electronic Commerce and Social Commerce, 4th ed., Cham, Switzerland: Springer, (2017), 67–100.

39.

Timmers

, Business Models for Electronic Markets, Electronic Markets 8(2) (1998), 3–8.

40.

Magretta

, Why Business Model Mater, Harvard Business Review (2002), 86–92.

41.

Osterwalder

, Pigneur

and Tucci

C.L.

, Clarifying Business Models: Origins, Present and Future of the Concept, Communications of the Association for Information Systems 16 (2005), 1–25. DOI: 10.1.1.83.7452

42.

Laosethakul

and Boulton

, Critical Success Factors For E-Commerce In Thailand: Cultural And Infrastructural Influences, The Electronic Journal on Information Systems in Developing Countries 30(1) (2007), 1–22. DOI: 10.1002/j.1681-4835.2007.tb00205.x

43.

Colla

and Lapoule

, E-commerce: exploring the critical success factors, International Journal of Retail & Distribution Management 40(11) (2012), 842–864. DOI: 10.1108/09590551211267601

44.

Turban

, Whiteside

, King

and Outland

, E-Commerce Strategy and Implementation, in Introduction to Electronic Commerce and Social Commerce, Cham, Switzerland: Springer, (2017), 383–414.

45.

Laudon

K.C.

and Travel

C.G.

, E-Commerce Business Strategy, in E-Commerce 2018, 14th Global ed., Harlow, England: Pearson, (2018), 326–381.

46.

İnci

, Consumer Perceptions and Buying Behaviors Towards “PRIVATE SHOPPING SITE” Business Model as an Online Retailing Method, Ph.D. dissertation, Business Administration, Marmara Üniversitesi, İstanbul, Turkey, (2014).

47.

Welling

and White

, Web site performance measurement: promise and reality, Managing Service Quality: An International Journal 16(6) (2006), 654–670. DOI: 10.1108/09604520610711954

48.

Yang

and Shi

, Analysis on Marketing Ability and Financial Performance in Internet Company, in 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Singapure, (2015).

49.

Mintzberg

and Lampel

, Reflecting on the Strategy Process, Sloan Management Review 40(3) (1999), 21–30.

50.

Baykal

, Strategic Management and Strategic Planning for Efficiency and Effectiveness in Organizations, Social Sciences Research Journal 7(2) (2018), 151–160.

51.

Çubukçu

, Strategy Types and Practice Examples Applied in Business, International Journal Of Management Academy 1(2) (2018), 142–156.

52.

Akan

, Allen

R.S.

, Helms

M.M.

and S.A.S. III, Critical tactics for implementing Porter’s generic strategies, Journal of Business Strategy 27(1) (2006), 43–53. DOI: 10.1108/02756660610640173

53.

Miller

, Configurations of Strategy and structure: Towards a Synthesis, Strategic Management Journal 7 (1986), 233–249.

54.

Porter

M.E.

, Competitive Strategy: The Core Concepts, in Competitive AdvantageCreating And Sustainining Superior Performance, New York, NY, USA: The Free Press, (1998), 1–32.

55.

Hitt

M.A.

, Ireland

R.D.

and Hoskisson

R.E.

, The External Environment: Opportunities, Threats, Industry Competition, and Competitor Analysis, in Strategic Management: Competitiveness & Globalization: Concept And Cases, 12. ed., Boston, MA, USA: Cengage Learning, (2016), 38–75.

56.

Porter

M.E.

, How Competitive Forces Shape Strategy, Harvard Business Review (1979), 137–145.

57.

Hitt

M.A.

, Ireland

R.D.

and Hoskisson

R.E.

, The Internal Organization: Resources, Capabilities, Core Competencies, and Competitive Advantages, in Strategic Management: Competitiveness & Globalization: Concept And Cases, 12. ed., Boston, MA, USA: Cengage Learning, (2016), 76–107.

58.

Porter

M.E.

and Millar

V.E.

, How Information Gives You Competitive Advantage, Harvard Business Review 63(4) (1985), 149–160.

59.

Bellman

R.E.

and Zadeh

L.A.

, Decision-Making in a Fuzzy Environment, Management Science 17(4) (1970), B141–B164.

60.

and Liao

, Intuitionistic Fuzzy Analytic Hierarchy Process, IEEE Transactions on Fuzzy Systems 22(4) (2014), 749–761. DOI: 10.1109/TFUZZ.2013.2272585

61.

Çinar

and Uygun

Ö.

, Selecting Green Supplier Using Intuitionistic Fuzzy AHP, Zeki Sistemler Teori ve Uygulamalari Dergisi 2(2) (2019), 24–31.

62.

Edjossan-Sossou

A.M.

, Galvez

, Deck

, Al Heib

, Verdel

, Dupont

, Chery

, Camargo

and Morel

, Sustainable risk management strategy selection using a fuzzy multi-criteria decision approach, International Journal of Disaster Risk Reduction 45 (2020), 1–15.

63.

Dinçer

, Baykal

and Yüksel

, Innovative capacity-based approach to blue ocean strategies of family firms: An IT2 fuzzy hybrid decision-making analysis for potential investors, Journal of Intelligent & Fuzzy Systems 37 (2019), 8459–8470.

64.

Paksoy

, Yapici Pehlivan

and Kahraman

, Organizational strategy development in distribution channel management using fuzzy AHP and hierarchical fuzzy TOPSIS, Expert Systems with Applications 39(3) (2012), 2822–2841.

65.

Aslani

, Marja

and Kekale

, Application of Creativity Techniques in the Creation of Organizational Strategies, International Journal of Engineering Business Management 4 (2012), 1–8.

66.

Karasan

, Erdogan

and Ilbahar

, Prioritization of production strategies of a manufacturing plant by using an integrated intuitionistic fuzzy AHP & TOPSIS approach, Journal of Enterprise Information 31(4) (2018), 510–528.

67.

Yue

and Yue

, A Soft Approach to Evaluate the Customer Satisfaction in E-retailing, in Fuzzy Information and Engineering and Decision, B. Cao, Ed., Cham, Springer, (2018), 282–296.

68.

Ilbahar

and Kahraman

, Retail Store Performance Measurement Using a Novel Interval-valued Pythagorean Fuzzy WASPAS Method’, Journal of Intelligent & Fuzzy Systems 35(3) (2018), 3835–3846.

69.

Rouyendegh

B.D.

, Yildizbasi

and Yilmaz

, Evaluation of retail industry performance ability through integrated intuitionistic fuzzy TOPSIS and data envelopment analysis approach, Soft Computing 24 (2020), 12255–12266.

70.

Davoudabadi

, Mousavi

S.M.

, Mohagheghi

and Vahdani

, Resilient Supplier Selection Through Introducing a New Interval-Valued Intuitionistic Fuzzy Evaluation and Decision-Making Framework, Arabian Journal for Science and Engineering 44 (2019), 7351–7360.

71.

Chang

K.-H.

, A novel supplier selection method that integrates the intuitionistic fuzzy weighted averaging method and a soft set with imprecise data, Annals of Operations Research 272 (2019), 139–157.

72.

Onar

S.Ç.

, Oztaysi

and Kahraman

, Dynamic intuitionistic fuzzy multi-attribute aftersales performance evaluation, Complex & Intelligent Systems 3 (2017), 197–204.

73.

Nakiboglu

and Bulgurcu

, Supplier selection in a Turkish textile company by using intuitionistic fuzzy decision-making, The Journal of The Textile Institute 112(2) (2021), 322–332.

74.

, Tian

and Buitrago

G.A.

, Evaluation and selection of Chinese government venture capital investment projects: A research based on analytic hierarchy process and intuitionistic fuzzy set–technique for order of preference by similarity to ideal solution method, Managerial and Decision Economics (2021), 1–15.

75.

Rouyendegh

B.D.

, Yildizbasi

and Üstünyer

, Intuitionistic Fuzzy TOPSIS method for green supplier selection problem, Soft Comput 24 (2020), 2215–2228.

76.

Dogan

, Deveci

, Canitez

and Kahraman

, A corridor selection for locating autonomous vehicles using, Soft Computing 24 (2020), 8937–8953.

77.

Kahraman

, Öztayşi

and Çevik Onar

, An Integrated Intuitionistic Fuzzy AHP and TOPSIS Approach to Evaluation of Outsource Manufacturers, De Gruyter 29(1) (2020), 283–297.

78.

Luo

, Chen

, Cen

, Pan

, Zhong

and He

, Research on comprehensive environmental impact assessment of shale gas development, Journal of Engineering, Design and Technology 18(1) (2020), 1–20.

79.

Büyüközkan

, Havle

C.A.

and Feyzioğlu

, A new digital service quality model and its strategic analysis in aviation industry using interval-valued intuitionistic fuzzy AHP, Journal of Air Transport Management 86 1–16. 2020.

80.

Tiwari

, Lohani

Q.D.

and Muhuri

P.K.

, Interval-valued Intuitionistic Fuzzy TOPSIS method for Supplier Selection Problem, in 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow, (2020).

81.

Kumar

and Anbanandam

, Location selection of multimodal freight terminal under STEEP sustainability, Research in Trasportation Business & Management 33 (2020), 1–16.

82.

Memari

, Dargi

, Jokar

M.R.A.

, Ahmad

and Rahim

A.R.A.

, Sustainable supplier selection: A multi-criteria intuitionistic fuzzy TOPSIS method, Journal of Manufacturing Systems 50 (2019), 9–24.

83.

Büyüközkan

, Feyzioğlu

and Havle

, Intuitionistic Fuzzy AHP Based Strategic Analysis of Service Quality in Digital Hospitality Industry, in 9th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2019, Berlin, (2019).

84.

Kumar

and Anbanandam

, Location selection of multimodal freight terminal under STEEP, Research in Transportation Business & Management 33 (2019), 1–16.

85.

Wind

and Saaty

T.L.

, Marketing Applications Of The Analytic Hierarchy Process, Management Science 26(7) (1980), 641–658.

86.

Saaty

T.L.

, How to Make a Decision: The Analytic Hierarchy Process, Interfaces 24(6) (1994), 19–43.

87.

Tzeng

G.-H.

and Huang

J.-J.

, TOPSIS and VIKOR, in Multiple Attribute Decision Making Methods And Applications, Boca Raton, Florida, USA: Taylor & Francis Group, 2011, pp. 69–80.

88.

Büyüközkan

and Güleryüz

, A new integrated intuitionistic fuzzy group decision making approach for product development partner selection, Computers & Industrial Engineering 102 (2016), 383–395. DOI: 10.1016/j.cie.2016.05.038

89.

Efe

, Boran

F.E.

and Kurt

, Ergonomic Product Concept Selection Using Intuitionistic Fuzzy, Suleyman Demirel University Journal of Engineering Sciences and Design 3(3) (2015), 433–440.

90.

Boran

F.E.

, An application of intuitionistic fuzzy set on personnel selection, Institute of Science and Technology, Gazi University, Ankara, (2009).

91.

Karakiş

, Bulanik AHP ve Bulanik TOPSIS ile Bütünleşik Karar Destek Modeli Önerisi: Özel Okullarda Öğretmen Seçimi, Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 53 (2019), 112–137.

92.

Kahraman

, Cebeci

and Ulukan

, Multi-criteria supplier selection using fuzzy AHP, Logistics Information Management 16(6) (2003), 382–394.

93.

Chang

D.-Y.

, Applications of the extent analysis method on fuzzy AHP, European Journal of Operational Research 95(3) (1996), 649–655.

94.

Aytaç

, Bulanik Çok Kriterli Karar Verme Yöntemleri İle Tedarikçi Seçimi ve Ekonomik Sipariş Miktarinin Tespiti: Otomotiv Sektöründe Bir Uygulama, Ph.D. dissertation, Fen Bilimleri Enstitüsü, Marmara Üniversitesi, İstanbul, Turkey, (2013).

95.

Alcan

, Balin

and Başligil

, Fuzzy multicriteria selection among cogeneration systems: A real case application, Energy and Buildings 67 (2013), 624–634.

DM	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10
DM1	MS	VS	S	LS	MS	VS	S	VS	MS	S
DM2	MS	LS	LS	VS	MS	MS	LS	LS	VS	VS
DM3	MS	VS	LS	S	MS	VS	LS	LS	VS	LS
DM4	VS	S	LS	VS	MS	VS	S	VS	VS	S
DM5	MS	MS	MS	MS	MS	MS	S	MS	MS	S
DM6	MS	VS	VS	VS	VS	VS	S	VS	MS	VS
DM7	MS	VS	S	VS	VS	VS	VS	S	MS	VS
DM8	S	S	S	VS	VS	S	U	U	LS	U
DM9	VS	S	LS	S	S	VS	LS	VS	S	U

DM	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10
DM1	MS	MS	S	MS	VS	MS	LS	MS	MS	MS
DM2	MS	S	U	S	MS	VS	MS	MS	VS	VS
DM3	MS	VS	LS	LS	S	VS	LS	LS	VS	VS
DM4	MS	MS	LS	VS	MS	MS	S	MS	VS	MS
DM5	MS	VS	VS	MS	MS	MS	VS	VS	VS	MS
DM6	VS	VS	VS	VS	VS	VS	VS	VS	VS	VS
DM7	VS	S	LS	S	S	VS	S	VS	VS	S
DM8	VS	VS	LS	S	S	VS	MS	MS	MS	MS
DM9	VS	VS	LS	S	S	VS	U	S	S	LS

DM	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10
DM1	VS	MS	S	MS	MS	MS	LS	MS	MS	VS
DM2	LS	VS	S	S	S	VS	VS	S	VS	S
DM3	MS	VS	LS	S	VS	VS	LS	LS	VS	MS
DM4	MS	VS	LS	MS	S	MS	S	MS	S	MS
DM5	MS	VS	S	VS	VS	VS	S	MS	MS	MS
DM6	VS	VS	VS	VS	VS	VS	VS	VS	VS	VS
DM7	MS	S	S	S	LS	VS	S	VS	VS	VS
DM8	MS	S	U	S	S	S	VS	VS	VS	VS
DM9	VS	VS	LS	LS	S	VS	LS	LS	LS	U

DM	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10
DM1	MS	VS	S	LS	MS	VS	S	VS	MS	S
DM2	MS	LS	LS	VS	MS	MS	LS	LS	VS	VS
DM3	MS	VS	LS	S	MS	VS	LS	LS	VS	LS
DM4	VS	S	LS	VS	MS	VS	S	VS	VS	S
DM5	MS	MS	MS	MS	MS	MS	S	MS	MS	S
DM6	MS	VS	VS	VS	VS	VS	S	VS	MS	VS
DM7	MS	VS	S	VS	VS	VS	VS	S	MS	VS
DM8	S	S	S	VS	VS	S	U	U	LS	U
DM9	VS	S	LS	S	S	VS	LS	VS	S	U

DM	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10
DM1	MS	MS	S	MS	VS	MS	LS	MS	MS	MS
DM2	MS	S	U	S	MS	VS	MS	MS	VS	VS
DM3	MS	VS	LS	LS	S	VS	LS	LS	VS	VS
DM4	MS	MS	LS	VS	MS	MS	S	MS	VS	MS
DM5	MS	VS	VS	MS	MS	MS	VS	VS	VS	MS
DM6	VS	VS	VS	VS	VS	VS	VS	VS	VS	VS
DM7	VS	S	LS	S	S	VS	S	VS	VS	S
DM8	VS	VS	LS	S	S	VS	MS	MS	MS	MS
DM9	VS	VS	LS	S	S	VS	U	S	S	LS

DM	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10
DM1	VS	MS	S	MS	MS	MS	LS	MS	MS	VS
DM2	LS	VS	S	S	S	VS	VS	S	VS	S
DM3	MS	VS	LS	S	VS	VS	LS	LS	VS	MS
DM4	MS	VS	LS	MS	S	MS	S	MS	S	MS
DM5	MS	VS	S	VS	VS	VS	S	MS	MS	MS
DM6	VS	VS	VS	VS	VS	VS	VS	VS	VS	VS
DM7	MS	S	S	S	LS	VS	S	VS	VS	VS
DM8	MS	S	U	S	S	S	VS	VS	VS	VS
DM9	VS	VS	LS	LS	S	VS	LS	LS	LS	U

Competitive strategy selection in retail E-Commerce using IF AHP & TOPSIS methodology

Abstract

Keywords

1 Introduction

2 Main concepts and literature review

2.1 Retail

2.3 Competition strategies

2.4 Other Issues

2.5 Fuzzy literature on competitive strategy selection

Table 4 Linguistic terms for DMs’ importance degrees of criteria Linguistic Terms Contraction Intuitionistic Fuzzy Value Expert E (0.90,0.10) Advanced A (0.75,0.20) Proficient P (0.50,0.45) Advanced Beginner AB (0.35,0.60) Novice N (0.10,0.90)

References

Table 4
Linguistic terms for DMs’ importance degrees of criteria

Linguistic Terms Contraction Intuitionistic Fuzzy Value

Expert E (0.90,0.10)

Advanced A (0.75,0.20)

Proficient P (0.50,0.45)

Advanced Beginner AB (0.35,0.60)

Novice N (0.10,0.90)

DM	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10
DM1	MS	VS	S	LS	MS	VS	S	VS	MS	S
DM2	MS	LS	LS	VS	MS	MS	LS	LS	VS	VS
DM3	MS	VS	LS	S	MS	VS	LS	LS	VS	LS
DM4	VS	S	LS	VS	MS	VS	S	VS	VS	S
DM5	MS	MS	MS	MS	MS	MS	S	MS	MS	S
DM6	MS	VS	VS	VS	VS	VS	S	VS	MS	VS
DM7	MS	VS	S	VS	VS	VS	VS	S	MS	VS
DM8	S	S	S	VS	VS	S	U	U	LS	U
DM9	VS	S	LS	S	S	VS	LS	VS	S	U

DM	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10
DM1	MS	MS	S	MS	VS	MS	LS	MS	MS	MS
DM2	MS	S	U	S	MS	VS	MS	MS	VS	VS
DM3	MS	VS	LS	LS	S	VS	LS	LS	VS	VS
DM4	MS	MS	LS	VS	MS	MS	S	MS	VS	MS
DM5	MS	VS	VS	MS	MS	MS	VS	VS	VS	MS
DM6	VS	VS	VS	VS	VS	VS	VS	VS	VS	VS
DM7	VS	S	LS	S	S	VS	S	VS	VS	S
DM8	VS	VS	LS	S	S	VS	MS	MS	MS	MS
DM9	VS	VS	LS	S	S	VS	U	S	S	LS

DM	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10
DM1	VS	MS	S	MS	MS	MS	LS	MS	MS	VS
DM2	LS	VS	S	S	S	VS	VS	S	VS	S
DM3	MS	VS	LS	S	VS	VS	LS	LS	VS	MS
DM4	MS	VS	LS	MS	S	MS	S	MS	S	MS
DM5	MS	VS	S	VS	VS	VS	S	MS	MS	MS
DM6	VS	VS	VS	VS	VS	VS	VS	VS	VS	VS
DM7	MS	S	S	S	LS	VS	S	VS	VS	VS
DM8	MS	S	U	S	S	S	VS	VS	VS	VS
DM9	VS	VS	LS	LS	S	VS	LS	LS	LS	U