## 1 Introduction

*et al.*, 2020). With technological advances, emerging new digital technologies have deeply altered the way people communicate and interact with their enclosing. Technological novelties and personal gadgets, such as 3D printing, internet of things, big data, cloud, augmented reality, personal computers, smartphones, self-driving cars, mobile devices, advanced television units, drones, smartwatches, and wearable devices change the way societies access and exchange information (Büyüközkan and Göçer, 2018a). These technologies will provide the digitalization of products and services and new business models (PwC Sweden, 2018). Although many organizations have initiated a digital transformation in supply chains, they have not tackled a holistic approach to their DSC and it have been caused this situation to be in initial development stages until now. Hence, the biggest obstacle to successful digital transformation in the supply chain is the lack of digital strategies in organizations (PwC Sweden, 2018). Digital strategy implementation focuses on the entire supply chain, addressing the questions of “how, where, when and by whom” goals and objectives will be achieved (Büyüközkan and Göçer, 2018a). Organizations need to evaluate their strategies according to certain criteria in order to obtain a successful digital transformation in the supply chain and to create a roadmap. But, there is a lack of a strategic road map to guide organizations in the literature. Therefore, there has been a need for a comprehensive strategic roadmap carefully identifying and planning the digital transformation of organizations. Besides, it is known that in the literature there is no evaluation of digital transformation strategies in the supply chain with a MCDM approach. For this emerging need, organizations should be evaluated by considering together more than one criteria and so, they must use a MCDM method. MCDM includes several main and sub criteria, which can be tangible or intangible and used to rank the alternatives during a decision process. There are numerous MCDM methods in the literature such as Analytic Network Process (ANP), Analytic Hierarchy Process (AHP), Best-Worst Method (BWM), Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS), Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Multi-Attributive Border Approximation area Comparison (MABAC) and others. AHP is one of widely used and most popular MCDM methods. AHP is based on pairwise comparisons and experts’ judgments (Saaty, 2008). AHP divides a huge and complex problem into smaller and easier problems which can be solved easily and then combine these sub-solutions to obtain the final solution of the main problem (Otay

*et al.*, 2017). Traditional AHP uses a linguistic scale of 1 to 9 with numerical values. However, according to Buckley (1985), a precise numerical representation of a linguistic term may not reflect the judgments in the minds of decision makers (DMs). For example, a linguistic assessment such as “Very Strong Significance” is expressed with a 7 on the traditional AHP scale. However, the DM’s “Very Strong Significance” decision cannot be certain enough to assign a “7”. With “Very Strong Significance”, DM can assign a corresponding fuzzy number such as (6.5, 7, 7.5). This may provide a better representation of the DM’s assessment. Fuzzy sets are excellent tools for overcoming such uncertainty (Otay

*et al.*, 2017). Fuzzy sets introduced by Zadeh (1965) are represented by membership degrees. Since its development, fuzzy sets have extended in various ways due to the lack of information and inability to handle the imprecise information of complex systems. Various extensions of ordinary fuzzy sets have been introduced in the literature to define membership functions in different ways (see Fig. 1). After type-2 fuzzy sets were introduced by Zadeh (1975), Intuitionistic fuzzy sets (IFSs) expressed with degrees of membership and non-membership have been proposed by Atannasov (1986). Later, Atannasov (1999) have introduced intuitionistic type-2 fuzzy sets (IFS2). After hesitant fuzzy sets (HFSs) were introduced by Torra (2010), IFS2 were extended by Yager (2013) to Pythagorean fuzzy sets (PFSs), which are represented by a larger area for membership degrees. After that, Yager (2017) introduced q-rung orthopair fuzzy sets, which is a general class of IFSs and PFSs. In IFSs the sum of membership and non-membership degrees should be at most one, in PFSs the sum of their squares should be at most one, and also for q-rung orthopair fuzzy sets, the sum of their

*q*th power have to equal at most to one. Yager stated that as q increases, the range of acceptable orthopair increases, thus giving the user more freedom to express his belief about the degree of membership. When $q=3$, Senapati and Yager (2020) have considered as fermatean fuzzy sets (FFSs) to q-rung orthopair fuzzy sets. They defined basic operations for the FFSs and introduced new score function and accuracy function for the ranking of FFSs. Besides, they developed a fermatean fuzzy TOPSIS method for handling the MCDM problem. Senapati and Yager (2019a) introduced Fermatean arithmetic mean operations, subtraction, division and developed a fermatean fuzzy weighted product model to solve the MCDM models. Then, Senapati and Yager (2019b) developed several fermatean fuzzy aggregation operators and proposed a MCDM approach by using new operators based on fermatean fuzzy conditions.

## 2 Digital Transformation Era and Strategies

*et al.*, 2016). Digital supply chains are capable of broad information availability and provide superior collaboration and communication between digital platforms, providing enhanced reliability, efficiency and agility (Raab and Griffin-Cryan, 2011). A successful digital transformation largely depends on the digital transformation of each partner in the value chain of organizations and all processes and information flows between these different partners. It also requires adopting a holistic view of the entire partner ecosystem.

### 2.1 Literature Review

*et al.*(2016a) presented an overview of the DSC management practices of leading companies in various industries, the DSC management concepts, and opportunities that arise from the application of digital technologies to supply chain management (SCM). Agrawal and Narain (2018) referred to its benefits by offering a framework of the digital supply chain. Scuotto

*et al.*(2017) explained the relationship between multiple buyers and suppliers in the context of SMEs’ DSC management. Farahani

*et al.*(2016b) provided the creation of the DSC management agenda by presenting 17 DSC management use cases identified by expert interviews. Korpela

*et al.*(2016) aimed to establish a DSC integration based on global standards. Bhargava

*et al.*(2013) proposed a new based approach for protecting shared data in DSCs. Pundir

*et al.*(2019) reviewed the suitability of complementary technologies such as IoT and Blockchain technology for DSC. Luthra and Mangla (2018) evaluated challenges to Industry 4.0 initiatives for supply chain sustainability in developing economies using an extensive literature review. Büyüközkan and Göçer (2017) presented an approach evaluating with intuitionistic fuzzy sets the supplier selection process in the DSC environment. Using the MOORA (Multi-Objective Optimization with Ratio Analysis) method, they realized a real case study to show the validity of the proposed approach. Alkan (2021) used the interval-valued Pythagorean fuzzy AHP method to assess the risks of digital transformation based on a sustainable supply chain. Tjahjono

*et al.*(2017) purposed to provide a thought towards Supply Chain 4.0 by presenting a preliminary analysis of the impact of Industry 4.0 on SCM. Ivanov

*et al.*(2019) reviewed how digital technologies and Industry 4.0 affect the ripple effect and performance of the supply chains. They presented the first study that connects information, business, analytics, engineering, and perspectives on digitalization and supply chain risks.

### 2.2 The Technological Enablers of DSC

*et al.*, 2020). Big data in DSC is to realize the necessary transparency by uncovering process interruptions and ensuring that changes are implemented quickly. Big data analytics provide better demand forecasting and planning, inventory planning and management, network, and routing optimization advanced procurement with collaborative optimization (Kearney, 2015; Alkan and Kahraman, 2021). Cloud computing is described as a style of computing in which scalable and flexible IT-enabled capabilities are presented as a service through internet technologies (Farahani

*et al.*, 2020). Cloud computing creates diverse business networks to enable companies to fully and rapidly engage with supply chain stakeholders (Kearney, 2015). The Internet of Things (IoT) is a network of physical objects that includes embedded technology to communicate, perceive, or interact with their internal and external environments (Farahani

*et al.*, 2020). IoT provides to open up to new business models and operational possibilities in the supply chains and respond to changing customer needs in real-time effectively. Tracking and tracing throughout the supply chain are provided through technologies underlying IoT such as Bluetooth, GSM (global system for mobile communication), and radio frequency identification (RFID) to rapidly evaluate and respond to changes in customer demand (WTO, 2019). Warehouse automation through advanced robotic technologies becomes much more holistic as some warehouses are fully connected to production loading points, so that all processes are carried out without manual intervention (Alicke

*et al.*, 2016). A three-dimensional scanner (3D) is a device creating object models of them by capturing data about the appearance and shape of real-world objects (Farahani

*et al.*, 2020). With 3D printing in the supply chain, the spare parts supply chain can be decreased to much fewer suppliers, even making own production possible. Thereby, 3D having an important impact on physical flows in the supply chain leads to faster delivery to the customer, lower labour unit and transport cost, and notably reduced inventory levels and costs in the supply chain (PwC Sweden, 2018). Augmented reality is defined as the situation that creates a new perception environment by combining computer-generated elements with the real world, in which users can interact (WTO, 2019). Augmented reality in the supply chain contributes to finding the right quantity of the right material much more efficiently by enabling better warehouse management (Kearney, 2015). Except for these technologies, GPS technology allows companies to take full control of shipping locations, while sensors control environmental conditions such as temperature and humidity and determine maintenance requirements (PwC Sweden, 2018). Autonomous and smart vehicles provide significant operational cost reductions in transportation and product handling, and also offer several benefits related to lower environmental costs and lead times (Alicke

*et al.*, 2016).

### 2.3 Key Challenges and Opportunities of Digital Supply Chain

##### Table 1

Sharing information | DSC provides sharing information about demand, manufacturing, inventories, and logistics capacity, and thus it enables much closer integration with customers by boosting the agility of the entire chain (Raab and Griffin-Cryan, 2011; Alicke et al., 2016; Schrauf and Berttram, 2016; Xu, 2014; Ivanov et al., 2019; WTO, 2019). |

Cross-functional relationship | Inter-functional cooperation between various elements in the organization provides to ensure the elimination of various bottlenecks, delays, or interruptions in the processes and to create a smooth flow within the organization (Raab and Griffin-Cryan, 2011; The Center for Global Enterprise, 2015; Farahani et al., 2020; Alicke et al., 2016; Schrauf and Berttram, 2016; Xu, 2014). |

Adoption of advanced analytical tools | Adoption of advanced analytical tools provide to gain a better understanding and forecasting of the demand and accelerate the decision-making process (The Center for Global Enterprise, 2015; Farahani et al., 2020; Schrauf and Berttram, 2016; Xu, 2014; Kearney, 2015; Gezgin et al., 2017). |

Supply chain visibility | Real-time visibility in the supply chain improves better DSC management by creating a coordinated end-to-end supply chain (Raab and Griffin-Cryan, 2011; Farahani et al., 2020; Agrawal and Narain, 2018; Schrauf and Berttram, 2016). |

Financial approach | Financial measurements enable quick execution of digital transformation efforts with less cost (The Center for Global Enterprise, 2015; Schrauf and Berttram, 2016; Kearney, 2015; Gezgin et al., 2017). |

Customer orientation | Customer orientation aims to offer personalized products by meeting customer expectations through end-to-end connectivity between suppliers and customers through cloud-based platforms (Alicke et al., 2016; Schrauf and Berttram, 2016; Xu, 2014; Kearney, 2015; Gezgin et al., 2017). |

Training and skills development | DSC requires providing employees with the necessary digital supply chain management skills to ensure an end-to-end understanding of value chain mechanics in digital transformation (Schrauf and Berttram, 2016; Xu, 2014; Luthra and Mangla, 2018; Gezgin et al., 2017). |

Digital culture | Digital culture is necessary for the adoption of a cultural change in the thinking of each member in the organization to realize end-to-end digital transformation (Schrauf and Berttram, 2016; Luthra and Mangla, 2018). |

Innovation | Digital supply chain helps a company strengthen business models through innovations in its designs and collaborates more effectively with both suppliers and customers (Farahani et al., 2020; Alicke et al., 2016; Schrauf and Berttram, 2016). |

Standardization | Identify the roles, duties and responsibilities of all parties in the digital supply chain and ensure that the terms of all agreements are clearly defined and agreed upon, as well as adopt a single set of global standards that support data exchange, processes and capabilities (Farahani et al., 2020; Xu, 2014; Luthra and Mangla, 2018; Kearney, 2015). |

Automation | Automated operations facilitate the work of supply chain professionals and increase operational efficiency by allowing them to focus on more valuable tasks (Farahani et al., 2020; Alicke et al., 2016; Schrauf and Berttram, 2016; Xu, 2014; Kearney, 2015; Gezgin et al., 2017). |

Integration | Integration enables simultaneous management of information and processes with all stakeholders in digital supply chain (The Center for Global Enterprise, 2015; Farahani et al., 2020; Alicke et al., 2016; Schrauf and Berttram, 2016; Xu, 2014; Kearney, 2015; Gezgin et al., 2017). |

Flexibility | Digitalization in the supply chain allows easy adaptation to change circumstances and quickly assess changes in end-customer demand (Raab and Griffin-Cryan, 2011; Farahani et al., 2020; Alicke et al., 2016; Schrauf and Berttram, 2016; Kearney, 2015). |

Enhanced response management | DSC increases the speed of responding to highly variable markets and changing customer needs (Farahani et al., 2020; Alicke et al., 2016; Schrauf and Berttram, 2016; Xu, 2014). |

Security and privacy | Security and privacy stand for the tools used to transform a factory into a smarter factor and a supply chain into smarter value chains by avoiding security vulnerabilities increasing with digitalization in the supply chain (The Center for Global Enterprise, 2015; Luthra and Mangla, 2018; Kearney, 2015). |

*et al.*, 2017).

## 3 Preliminaries: Intuitionistic, Pythagorean, and Fermatean Fuzzy Sets

### 3.1 Intuitionistic Fuzzy Sets (IFSs)

##### Definition 3.1.

*X*be a non-empty set. An IFS

*I*in

*X*is given by:

##### (1)

\[ I=\big\{\big(x,{\mu _{I}}(x),{\nu _{I}}(x)\big)\hspace{0.1667em}\big|\hspace{0.1667em}x\epsilon X\big\},\]*I*with the condition that The hesitancy degree is calculated as follows:

##### Definition 3.2.

### 3.2 Pythagorean Fuzzy Sets (PFSs)

##### Definition 3.3.

*X*be a non-empty set. A Pythagorean fuzzy set

*P*in

*X*is an object having the form (Zhang and Xu, 2014):

##### (6)

\[ P=\big\{\big\langle x,{\mu _{P}}(x),{\nu _{P}}(x)\big\rangle \hspace{0.1667em}\big|\hspace{0.1667em}x\epsilon X\big\},\]*P*and it holds that: The hesitancy degree is calculated as follows:

##### Definition 3.4.

### 3.3 Fermatean Fuzzy Sets (FFSs)

*q*th power of membership and non-membersip degrees q-rung orthopair fuzzy sets is bounded with one. When $q=3$, Senapati and Yager (2020) have called q-rung orthopair fuzzy sets as fermatean fuzzy sets (see Fig. 2).

##### Definition 3.5.

*X*be a universe of discourse. A fermatean fuzzy set $\mathcal{F}$ in

*X*is an object having the form (Senapati and Yager, 2020):

##### (11)

\[ \mathcal{F}=\big\{\big\langle x,{\mu _{F}}(x),{\nu _{F}}(x)\big\rangle \hspace{0.1667em}\big|\hspace{0.1667em}x\epsilon X\big\},\]*x*in the set $\mathcal{F}$.

##### Definition 3.6.

##### Definition 3.7.

##### (16)

\[\begin{aligned}{}& {\mathcal{F}_{1}}\oplus {\mathcal{F}_{2}}=\Big(\sqrt[3]{{\mu _{F1}^{3}}+{\mu _{F2}^{3}}-{\mu _{F1}^{3}}{\mu _{F2}^{3}}},{\nu _{F1}}{\nu _{F2}}\Big),\end{aligned}\]##### (17)

\[\begin{aligned}{}& {\mathcal{F}_{1}}\otimes {\mathcal{F}_{2}}=\Big({\mu _{F1}}{\mu _{F2}},\sqrt[3]{{\nu _{F1}^{3}}+{\nu _{F2}^{3}}-{\nu _{F1}^{3}}{\nu _{F2}^{3}}}\hspace{0.1667em}\Big),\end{aligned}\]##### Definition 3.8.

##### Definition 3.9.

### 3.4 Interval-Valued Fermatean Fuzzy Sets (IVFFSs)

##### Definition 3.10.

*X*be a fixed set. An IVFFSs $\tilde{\mathcal{F}}$ in

*X*is an object having the form

##### (22)

\[ \tilde{\mathcal{F}}=\big\{\big\langle x,{\mu _{\tilde{\mathcal{F}}}}(x),{\nu _{\tilde{\mathcal{F}}}}(x)\big\rangle \hspace{0.1667em}\big|\hspace{0.1667em}x\epsilon X\big\},\]##### (25)

\[ 0\leqslant {\big({\mu _{\tilde{\mathcal{F}}}^{U}}(x)\big)^{3}}+{\big({\upsilon _{\tilde{\mathcal{F}}}^{U}}(x)\big)^{3}}\leqslant 1.\]##### Definition 3.11.

##### (26)

\[\begin{aligned}{}& {\tilde{\mathcal{F}}_{1}}\oplus {\tilde{\mathcal{F}}_{2}}=\Bigg(\Bigg[\begin{array}{l}\sqrt[3]{{({\mu _{{\tilde{\mathcal{F}}_{1}}}^{L}})^{3}}+{({\mu _{{\tilde{\mathcal{F}}_{2}}}^{L}})^{3}}-{({\mu _{{\tilde{\mathcal{F}}_{1}}}^{L}})^{3}}{({\mu _{{\tilde{\mathcal{F}}_{2}}}^{L}})^{3}}},\\ {} \sqrt[3]{{({\mu _{{\tilde{\mathcal{F}}_{1}}}^{U}})^{3}}+{({\mu _{{\tilde{\mathcal{F}}_{2}}}^{U}})^{3}}-{({\mu _{{\tilde{\mathcal{F}}_{1}}}^{U}})^{3}}{({\mu _{{\tilde{\mathcal{F}}_{2}}}^{U}})^{3}}}\end{array}\Bigg],\big[{\upsilon _{{\tilde{\mathcal{F}}_{1}}}^{L}}{\upsilon _{{\tilde{\mathcal{F}}_{2}}}^{L}},{\upsilon _{{\tilde{\mathcal{F}}_{1}}}^{U}}{\upsilon _{{\tilde{\mathcal{F}}_{2}}}^{U}}\big]\Bigg),\end{aligned}\]##### (27)

\[\begin{aligned}{}& {\tilde{\mathcal{F}}_{1}}\otimes {\tilde{\mathcal{F}}_{2}}=\Bigg(\big[{\mu _{{\tilde{\mathcal{F}}_{1}}}^{L}}{\mu _{{\tilde{\mathcal{F}}_{2}}}^{L}},{\mu _{{\tilde{\mathcal{F}}_{1}}}^{U}}{\mu _{{\tilde{\mathcal{F}}_{2}}}^{U}}\big],\Bigg[\begin{array}{l}\sqrt[3]{{({\upsilon _{{\tilde{\mathcal{F}}_{1}}}^{L}})^{3}}+{({\upsilon _{{\tilde{\mathcal{F}}_{2}}}^{L}})^{3}}-{({\upsilon _{{\tilde{\mathcal{F}}_{1}}}^{L}})^{3}}{({\upsilon _{{\tilde{\mathcal{F}}_{2}}}^{L}})^{3}}},\\ {} \sqrt[3]{{({\upsilon _{{\tilde{\mathcal{F}}_{1}}}^{U}})^{3}}+{({\upsilon _{{\tilde{\mathcal{F}}_{2}}}^{U}})^{3}}-{({\upsilon _{{\tilde{\mathcal{F}}_{1}}}^{U}})^{3}}{({\upsilon _{{\tilde{\mathcal{F}}_{2}}}^{U}})^{3}}}\end{array}\Bigg]\Bigg),\end{aligned}\]##### (28)

\[\begin{aligned}{}& \lambda \tilde{\mathcal{F}}=\Big(\Big[\sqrt[3]{1-{\big(1-{\big({\mu _{\tilde{\mathcal{F}}}^{L}}\big)^{3}}\big)^{\lambda }}},\sqrt[3]{1-{\big(1-{\big({\mu _{\tilde{\mathcal{F}}}^{U}}\big)^{3}}\big)^{\lambda }}}\Big],\big[{\big({\upsilon _{\tilde{\mathcal{F}}}^{L}}\big)^{\lambda }},{\big({\upsilon _{\tilde{\mathcal{F}}}^{U}}\big)^{\lambda }}\big]\Big),\end{aligned}\]##### (29)

\[\begin{aligned}{}& {\tilde{\mathcal{F}}^{\lambda }}=\Big(\big[{\big({\mu _{\tilde{\mathcal{F}}}^{L}}\big)^{\lambda }},{\big({\mu _{\tilde{\mathcal{F}}}^{U}}\big)^{\lambda }}\big],\Big[\sqrt[3]{1-{\big(1-{\big({\upsilon _{\tilde{\mathcal{F}}}^{L}}\big)^{3}}\big)^{\lambda }}},\sqrt[3]{1-{\big(1-{\big({\upsilon _{\tilde{\mathcal{F}}}^{U}}\big)^{3}}\big)^{\lambda }}}\hspace{0.1667em}\Big]\Big).\end{aligned}\]##### Definition 3.12.

##### (30)

\[\begin{aligned}{}& \textit{IVFFWA}({\tilde{\mathcal{F}}_{1}},{\tilde{\mathcal{F}}_{2}},\dots ,{\tilde{\mathcal{F}}_{n}})\\ {} & \hspace{1em}=\Bigg(\Bigg[\sqrt[3]{\Bigg(1-{\prod \limits_{i=1}^{n}}{\big(1-{\big({\mu _{{\tilde{\mathcal{F}}_{i}}}^{L}}\big)^{3}}\big)^{{w_{i}}}}\Bigg),}\sqrt[3]{\Bigg(1-{\prod \limits_{i=1}^{n}}{\big(1-{\big({\mu _{{\tilde{\mathcal{F}}_{i}}}^{U}}\big)^{3}}\big)^{{w_{i}}}}\Bigg)}\Bigg],\\ {} & \hspace{2em}\times \Bigg[{\prod \limits_{i=1}^{n}}{\big({\upsilon _{{\tilde{\mathcal{F}}_{i}}}^{L}}\big)^{{w_{i}}}},{\prod \limits_{i=1}^{n}}{\big({\upsilon _{{\tilde{\mathcal{F}}_{i}}}^{U}}\big)^{{w_{i}}}}\Bigg]\Bigg).\end{aligned}\]##### Definition 3.13.

##### (31)

\[\begin{aligned}{}& \textit{IVFFWG}({\tilde{\mathcal{F}}_{1}},{\tilde{\mathcal{F}}_{2}},\dots ,{\tilde{\mathcal{F}}_{n}})\\ {} & \hspace{1em}=\Bigg(\Bigg[{\prod \limits_{i=1}^{n}}{\big({\mu _{i}^{L}}\big)^{{w_{i}}}},{\prod \limits_{i=1}^{n}}{\big({\mu _{i}^{U}}\big)^{{w_{i}}}}\Bigg],\\ {} & \hspace{2em}\times \Bigg[\sqrt[\mathbf{3}]{\Bigg(1-{\prod \limits_{i=1}^{n}}{\big(1-{\big({\upsilon _{{\tilde{\mathcal{F}}_{i}}}^{L}}\big)^{3}}\big)^{{w_{i}}}}\Bigg)},\sqrt[3]{\Bigg(1-{\prod \limits_{i=1}^{n}}{\big(1-{\big({\upsilon _{{\tilde{\mathcal{F}}_{i}}}^{U}}\big)^{3}}\big)^{{w_{i}}}}\Bigg)}\Bigg]\Bigg).\end{aligned}\]##### Definition 3.14.

##### (32)

\[ \mathrm{Deff}({\tilde{\mathcal{F}}_{i}})=\left\{\begin{array}{l}\frac{1+|{({\mu _{i}^{L}})^{3}}-{({\nu _{i}^{L}})^{3}}|+1+|{({\mu _{i}^{U}})^{3}}-{({\nu _{i}^{U}})^{3}}|-{({\pi _{ij}^{L}})^{3}}-{({\pi _{ij}^{U}})^{3}}}{4}\times 10,\\ {} \hspace{1em}\textit{EI}\leqslant \textit{IVFFN}\leqslant \textit{CHI},\\ {} \frac{1}{\big(\frac{1+|{({\mu _{ij}^{L}})^{3}}-{({\nu _{ij}^{L}})^{3}}|+1+|{({\mu _{ij}^{U}})^{3}}-{({\nu _{ij}^{U}})^{3}}|-{({\pi _{ij}^{L}})^{3}}-{({\pi _{ij}^{U}})^{3}}}{4}\times 10\big)},\\ {} \hspace{1em}\textit{SLI}\leqslant \textit{IVFFN}\leqslant \textit{CLI}.\end{array}\right.\]## 4 A Novel Fermatean Fuzzy Analytic Hierarchy Process Method

*et al.*(2016) developed both interval-valued type-2 fuzzy AHP method and a new ranking method based on type-2 fuzzy sets by handling a supplier selection problem. Sadiq and Tesfamariam (2009) developed intuitionistic fuzzy AHP to handle vagueness and uncertainties in decision-making process. Wu

*et al.*(2013) developed a score function based on interval-valued intuitionistic fuzzy numbers (IVIFNs) and proposed a new interval-valued intuitionistic fuzzy AHP (IVIF-AHP) method for MCDM problems. Öztaysi

*et al.*(2015) developed the hesitant fuzzy AHP where the evaluations of experts are aggregated by ordered weighted averaging (OWA) operator. Gul (2018) proposed a new approach integrated Pythagorean fuzzy AHP and fuzzy VIKOR for risk assessment in the field of occupational health and safety. The Pythagorean fuzzy AHP has been used for weighting of the risk parameters. Then, fuzzy VIKOR has been applied to prioritize the hazards. Büyüközkan and Göçer (2019) proposed a new approach integrating AHP and complex proportional assessment (COPRAS) based on Pythagorean fuzzy sets to evaluate the digital supply chain partner selection. Karasan

*et al.*(2019) developed a new Pythagorean fuzzy AHP method and compared it with ordinary fuzzy AHP, revealing that the developed method produces consistent results that better represent the uncertainty of the decision-making environment. Abdel-Basset

*et al.*(2017) proposed a neutrosophic AHP method by using the triangular neutrosophic numbers for each pairwise comparison judgment. Bolturk and Kahraman (2018) proposed a new interval-valued neutrosophic AHP method and interval-valued neutrosophic AHP (IVN-AHP) based on cosine similarity measures. The proposed methods provide a scoring procedure for pairwise comparison matrices based on neutrosophic numbers. Garg

*et al.*(2021) developed complex interval-valued q-rung orthopair fuzzy sets (CIVq-ROFSs) and then developed averaging aggregation operator and geometric aggregation operators based on CIVq-ROFSs. They proposed AHP and TOPSIS methods based on CIVq-ROFSs. Kutlu Gündoğdu

*et al.*(2021) introduced a new hybrid picture fuzzy analytic hierarchy process and linear assignment model. The hybrid picture fuzzy AHP-linear assignment model validated with a comparative analysis. Mathew

*et al.*(2020) presented a novel approach integrating AHP and TOPSIS based on spherical fuzzy sets. They proposed a novel spherical fuzzy geometric mean formula for calculating the spherical fuzzy criteria weights and also presented a novel eleven-point spherical fuzzy linguistic term scale. Kahraman

*et al.*(2020) presented a literature review of studies on the integration of fuzzy AHP with other fuzzy multi-criteria methods. Duan

*et al.*(2021) presented some fundamental operations based on q-rung orthopair double hierarchy linguistic term sets (q-RODHLTS) and developed AHP method under q-RODHLTS. The distribution of fuzzy AHP publications from past to present analysed by using the Scopus database is illustrated in Fig. 3. As it is seen, engineering is the most researched scientific field in the literature, followed by computer science, mathematics and business, management and accounting research fields.

### 4.1 Proposed Method: IVFF-AHP

**Step 1:**Construct the hierarchical structure by determining the criteria and alternatives.

*m*decision criteria of set ${C_{j}}=\{{C_{1}},{C_{2}},\dots ,{C_{m}}\}$, with $j=1,2,\dots ,m$. Let ${w_{j}}=({w_{1}},{w_{2}},\dots ,{w_{m}})$ be the vector set used for defining the criteria weights, where ${w_{j}}>0$ and ${\textstyle\sum _{j=1}^{n}}{w_{j}}=1$. Table 2 presents linguistic terms and their corresponding interval-valued fermatean fuzzy numbers (IVFFNs).

**Step 2**: Construct the pairwise comparison matrix $Z={({z_{ij}})_{m\times m}}$ based on the opinions of experts given in Table 2.

##### (33)

\[ Z=\left[\begin{array}{c@{\hskip4.0pt}c@{\hskip4.0pt}c@{\hskip4.0pt}c}1\hspace{1em}& {z_{12}}\hspace{1em}& \cdots \hspace{1em}& {z_{1m}}\\ {} {z_{21}}\hspace{1em}& 1\hspace{1em}& \cdots \hspace{1em}& {z_{2m}}\\ {} \vdots \hspace{1em}& \vdots \hspace{1em}& \ddots \hspace{1em}& \vdots \\ {} {z_{m1}}\hspace{1em}& {z_{m2}}\hspace{1em}& \cdots \hspace{1em}& 1\end{array}\right]\hspace{1em}\text{where}\hspace{2.5pt}{z_{ij}}=\left\langle \big[{\mu _{ij}^{L}},{\mu _{ij}^{U}}\big],\big[{\nu _{ij}^{L}},{\nu _{ij}^{U}}\big]\right\rangle .\]**Step 3:**Check for the consistency of each pairwise comparison matrix $(Z)$. Here, to measure the consistency of expert judgments, match the crisp numbers obtained after defuzzifying to IVFFNs given in Table 2 based on Saaty’s scale. Then, apply the Saaty’s classical consistency process.

##### Table 2

Linguistic terms | IVFFN equivalents | |||

${\mu _{L}}$ | ${\mu _{U}}$ | ${\nu _{L}}$ | ${\upsilon _{U}}$ | |

Certainly High Importance (CHI) | 0.95 | 1 | 0 | 0 |

Very High Importance (VHI) | 0.8 | 0.9 | 0.1 | 0.2 |

High Importance (HI) | 0.7 | 0.8 | 0.2 | 0.3 |

Slightly More Importance (SMI) | 0.6 | 0.65 | 0.35 | 0.4 |

Equally Importence (EI) | 0.5 | 0.5 | 0.5 | 0.5 |

Slightly Less Importance (SLI) | 0.35 | 0.4 | 0.6 | 0.65 |

Low Importance (LI) | 0.2 | 0.3 | 0.7 | 0.8 |

Very Low Importance (VLI) | 0.1 | 0.2 | 0.8 | 0.9 |

Certainly Low Importance (CLI) | 0 | 0 | 0.95 | 1 |

**Step 4:**Aggregate the judgments of experts.

##### (34)

\[\begin{aligned}{}& \textit{IVPFWG}({z_{1}},{z_{2}},\dots ,{z_{k}})\\ {} & \hspace{1em}=\Bigg(\Bigg[{\prod \limits_{k=1}^{K}}{\big({\mu _{k}^{L}}\big)^{{w_{k}}}},{\prod \limits_{k=1}^{K}}{\big({\mu _{k}^{U}}\big)^{{w_{k}}}}\Bigg],\\ {} & \hspace{2em}\times \Bigg[\sqrt[3]{\Bigg(1-{\prod \limits_{k=1}^{K}}{\big(1-{\big({\upsilon _{k}^{L}}\big)^{3}}\big)^{{w_{k}}}}\Bigg)},\sqrt[3]{\Bigg(1-{\prod \limits_{k=1}^{K}}{\big(1-{\big({\upsilon _{k}^{U}}\big)^{3}}\big)^{{w_{k}}}}\Bigg)}\Bigg]\Bigg).\end{aligned}\]**Step 5:**Find the differences matrix $D={({d_{ij}})_{m\times m}}$ between lower and upper points of the membership and non-membership functions using Eqs. (35) and (36):

**Step 7:**Obtain the indeterminacy value $T={({t_{ij}})_{m\times m}}$ of the ${z_{ij}}$ using Eq. (39):

**Step 8:**Multiply the indeterminacy degrees with $S={({s_{ij}})_{m\times m}}$ matrix to obtain the matrix of unnormalized weights $R={({r_{ij}})_{m\times m}}$ using Eq. (40):

**Step 10.**Rank the alternatives based on the normalized priority weights obtained in Step 9.

## 5 Application

### 5.1 Problem Definition

### 5.2 Problem Solution

*DC- Digital Competence, O- Organizational, and M- Management*. The sub-criteria are listed as

*DC1- Digital Culture*,

*DC2- Security and Privacy*,

*DC3- Automation*,

*DC4- Standardization*,

*DC5- Innovation*,

*O1- Sharing Information*,

*O2- Cross-Functional Relationship*,

*O3- Integration*,

*O4- Training and Skills Development*,

*M1- Adoption of Advanced Analytical Tools*,

*M2- Supply Chain Visibility*,

*M3- Financial Orientation*,

*M4- Customer Orientation*,

*M5-Flexibility*, and

*M6- Enhanced Response*. Alternative strategies are

*A1- Human Resource Management and Talent-Based Strategies*,

*A2- Demand-Based Strategies*,

*A3- New Business Models-Based Strategies*, and

*A4- Technology and IT-Based Strategies*. Fig. 5 illustrates this hierarchical structure involving the main criteria, sub-criteria, and alternatives. These alternatives and criteria are evaluated by constructing pairwise comparison matrices through linguistic terms given in Table 2 by three experts. The pairwise comparison matrices consisting of linguistic terms for the main criteria, sub-criteria, and alternatives are presented with the consistency ratio in Tables 3–21. The consistency ratios of the pairwise comparison matrices are calculated using the linguistic scale and corresponding numerical values given in Table 2. Due to space constraints, the next steps of the developed method are shown on the main criteria. After linguistic expressions in the pairwise comparison, matrices are converted to IVFFNs using the relevant scale, each expert’s assessment is aggregated with the IVFFWG operator. Table 22 presents the aggregated IVFF values of the main criteria. Then, IVFF-AHP is used to obtain the weights of criteria and alternatives. Table 23 gives the difference matrix $D={({d_{ij}})_{m\times m}}$ between lower and upper values of the membership and non-membership degrees calculated based on Eqs. (35) and (36). The interval multiplicative matrix $S={({s_{ij}})_{m\times m}}$ given in Table 24 is calculated based on Eqs. (37) and (38) in Step 6. The matrix of weights before normalization $R={({r_{ij}})_{m\times m}}$ presented in Table 25 is obtained based on Eq. (40) in Step 8 by using the indeterminacy values given in Eq. (39). Then, the priority weights of each criterion obtained by using Eq. (41) in Step 9 and the final overall criteria weights are presented in Table 26. The overall criteria weights are obtained by multiplying the weights of the related main criteria and sub-criteria. Table 27 presents the priority weights of the alternatives according to the evaluation criteria. Finally, according to score values and ranking of alternatives demonstrated in Table 28, A2 is selected as the most suitable alternative.

*Demand-Based Strategies*should be adapted with the largest priority, followed by

*New Business Models-Based Strategies*,

*Technology and IT-Based Strategies*, and

*Human Resource Management*and

*Talent-Based Strategies*.

##### Table 3

E1 | E2 | E3 | |||||||

DC | $\text{O}$ | $\text{M}$ | DC | $\text{O}$ | $\text{M}$ | DC | $\text{O}$ | $\text{M}$ | |

DC |
EI | SMI | SLI | EI | SMI | SLI | EI | HI | SLI |

O |
SLI | EI | LI | SLI | EI | VLI | LI | EI | VLI |

M |
SMI | HI | EI | SMI | VHI | EI | SMI | VHI | EI |

CR |
0.033 | 0.006 | 0.056 |

##### Table 4

E1 | E2 | E3 | |||||||||||||

DC1 | DC2 | DC3 | DC4 | DC5 | DC1 | DC2 | DC3 | DC4 | DC5 | DC1 | DC2 | DC3 | DC4 | DC5 | |

DC1 |
EI | VHI | HI | VHI | SMI | EI | HI | SMI | VHI | SMI | EI | HI | SMI | VHI | EI |

DC2 |
VLI | EI | LI | SMI | LI | LI | EI | LI | EI | LI | LI | EI | SLI | SMI | VLI |

DC3 |
LI | HI | EI | HI | SLI | SLI | HI | EI | VHI | EI | SLI | SMI | EI | HI | SLI |

DC4 |
VLI | SLI | LI | EI | VLI | VLI | EI | VLI | EI | VLI | VLI | SLI | LI | EI | VLI |

DC5 |
SLI | HI | SMI | VHI | EI | SLI | HI | EI | VHI | EI | EI | VHI | SMI | VHI | EI |

CR |
0.098 | 0.047 | 0.035 |

##### Table 5

E1 | E2 | E3 | ||||||||||

O1 | O2 | O3 | O4 | O1 | O2 | O3 | O4 | O1 | O2 | O3 | O4 | |

O1 |
EI | HI | EI | HI | EI | HI | SLI | VHI | EI | SMI | SLI | HI |

O2 |
LI | EI | LI | SLI | LI | EI | LI | SMI | SLI | EI | LI | SMI |

O3 |
EI | HI | EI | HI | SMI | HI | EI | VHI | SMI | HI | EI | VHI |

O4 |
LI | SMI | LI | EI | VLI | SLI | VLI | EI | LI | SLI | VLI | EI |

CR |
0.059 | 0.086 | 0.044 |

##### Table 6

E1 | E2 | E3 | ||||||||||||||||

M1 | M2 | M3 | M4 | M5 | M6 | M1 | M2 | M3 | M4 | M5 | M6 | M1 | M2 | M3 | M4 | M5 | M6 | |

M1 |
EI | SMI | CHI | EI | VHI | HI | EI | SMI | CHI | EI | HI | SMI | EI | EI | VHI | SLI | HI | SMI |

M2 |
SLI | EI | VHI | SLI | HI | HI | SLI | EI | VHI | EI | HI | SMI | EI | EI | VHI | SLI | VHI | HI |

M3 |
CLI | VLI | EI | VLI | SLI | SLI | CLI | VLI | EI | VLI | SLI | LI | VLI | VLI | EI | CLI | SLI | SLI |

M4 |
EI | SMI | VHI | EI | VHI | HI | EI | EI | VHI | EI | VHI | HI | SMI | SMI | CHI | EI | HI | SMI |

M5 |
VLI | LI | SMI | VLI | EI | SLI | LI | LI | SMI | VLI | EI | SLI | LI | VLI | SMI | LI | EI | EI |

M6 |
LI | LI | SMI | LI | SMI | EI | SLI | SLI | HI | LI | SMI | EI | SLI | LI | SMI | SLI | EI | EI |

CR |
0.055 | 0.046 | 0.05 |

##### Table 7

DC1 | E1 | E2 | E3 | |||||||||

A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | |

A1 |
EI | VHI | HI | EI | EI | HI | HI | SMI | EI | VHI | HI | SLI |

A2 |
VLI | EI | LI | LI | LI | EI | SLI | LI | VLI | EI | SLI | VLI |

A3 |
LI | HI | EI | SLI | LI | SMI | EI | SLI | LI | SMI | EI | LI |

A4 |
EI | HI | SMI | EI | SLI | HI | SMI | EI | SMI | VHI | HI | EI |

CR |
0.079 | 0.075 | 0.086 |

##### Table 8

DC2 | E1 | E2 | E3 | |||||||||

A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | |

A1 |
EI | SLI | LI | CLI | EI | SLI | LI | CLI | EI | LI | SLI | CLI |

A2 |
SMI | EI | SLI | VLI | SMI | EI | LI | VLI | HI | EI | SMI | LI |

A3 |
HI | SMI | EI | LI | HI | HI | EI | SLI | SMI | SLI | EI | VLI |

A4 |
CHI | VHI | HI | EI | CHI | VHI | SMI | EI | CHI | HI | VHI | EI |

CR |
0.064 | 0.067 | 0.064 |

##### Table 9

DC3 | E1 | E2 | E3 | |||||||||

A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | |

A1 |
EI | VLI | VLI | CLI | EI | LI | LI | CLI | EI | VLI | VLI | CLI |

A2 |
VHI | EI | EI | LI | HI | EI | SMI | SLI | VHI | EI | EI | LI |

A3 |
VHI | EI | EI | LI | HI | SLI | EI | SLI | VHI | EI | EI | SLI |

A4 |
CHI | HI | HI | EI | CHI | SMI | SMI | EI | CHI | HI | SMI | EI |

CR |
0.09 | 0.071 | 0.068 |

##### Table 10

DC4 | E1 | E2 | E3 | |||||||||

A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | |

A1 |
EI | SLI | LI | LI | EI | LI | LI | VLI | EI | SLI | LI | LI |

A2 |
SMI | EI | SLI | LI | HI | EI | SLI | LI | SMI | EI | LI | LI |

A3 |
HI | SMI | EI | SLI | HI | SMI | EI | SLI | HI | HI | EI | EI |

A4 |
HI | HI | SMI | EI | VHI | HI | SMI | EI | HI | HI | EI | EI |

CR |
0.075 | 0.093 | 0.059 |

##### Table 11

DC5 | E1 | E2 | E3 | |||||||||

A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | |

A1 |
EI | VLI | VLI | LI | EI | CLI | CLI | VLI | EI | LI | VLI | VLI |

A2 |
VHI | EI | EI | SMI | CHI | EI | EI | HI | HI | EI | SLI | SLI |

A3 |
VHI | EI | EI | SMI | CHI | EI | EI | HI | VHI | SMI | EI | EI |

A4 |
HI | SLI | SLI | EI | VHI | LI | LI | EI | VHI | SMI | EI | EI |

CR |
0.028 | 0.093 | 0.028 |

##### Table 12

O1 | E1 | E2 | E3 | |||||||||

A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | |

A1 |
EI | LI | SLI | HI | EI | VLI | LI | SMI | EI | VLI | VLI | HI |

A2 |
HI | EI | SMI | CHI | VHI | EI | SMI | VHI | VHI | EI | EI | CHI |

A3 |
SMI | SLI | EI | VHI | HI | SLI | EI | HI | VHI | EI | EI | CHI |

A4 |
LI | CLI | VLI | EI | SLI | VLI | LI | EI | LI | CLI | CLI | EI |

CR |
0.065 | 0.088 | 0.094 |

##### Table 13

O2 | E1 | E2 | E3 | |||||||||

A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | |

A1 |
EI | SLI | SLI | HI | EI | LI | SLI | HI | EI | SLI | LI | HI |

A2 |
SMI | EI | EI | CHI | HI | EI | SMI | CHI | SMI | EI | SLI | VHI |

A3 |
SMI | EI | EI | CHI | SMI | SLI | EI | VHI | HI | SMI | EI | VHI |

A4 |
LI | CLI | CLI | EI | LI | CLI | VLI | EI | LI | VLI | VLI | EI |

CR |
0.012 | 0.065 | 0.091 |

##### Table 14

O3 | E1 | E2 | E3 | |||||||||

A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | |

A1 |
EI | SLI | EI | HI | EI | SLI | SMI | HI | EI | SLI | SMI | VHI |

A2 |
SMI | EI | SMI | VHI | SMI | EI | SMI | VHI | SMI | EI | HI | VHI |

A3 |
EI | SLI | EI | VHI | SLI | SLI | EI | HI | SLI | LI | EI | HI |

A4 |
LI | VLI | VLI | EI | LI | VLI | LI | EI | VLI | VLI | LI | EI |

CR |
0.045 | 0.086 | 0.091 |

##### Table 15

O4 | E1 | E2 | E3 | |||||||||

A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | |

A1 |
EI | CHI | HI | HI | EI | VHI | HI | HI | EI | VHI | HI | HI |

A2 |
CLI | EI | SLI | SLI | VLI | EI | LI | SLI | VLI | EI | SLI | EI |

A3 |
LI | SMI | EI | SMI | LI | HI | EI | SMI | LI | SMI | EI | SMI |

A4 |
LI | SMI | SLI | EI | LI | SMI | SLI | EI | LI | EI | SLI | EI |

CR |
0.071 | 0.071 | 0.045 |

##### Table 16

M1 | E1 | E2 | E3 | |||||||||

A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | |

A1 |
EI | CLI | LI | LI | EI | CLI | LI | LI | EI | CLI | LI | VLI |

A2 |
CHI | EI | HI | SMI | CHI | EI | SMI | SMI | CHI | EI | HI | SMI |

A3 |
HI | LI | EI | SLI | HI | SLI | EI | EI | HI | LI | EI | SLI |

A4 |
HI | SLI | SMI | EI | HI | SLI | EI | EI | VHI | SLI | SMI | EI |

CR |
0.07 | 0.012 | 0.065 |

##### Table 17

M2 | E1 | E2 | E3 | |||||||||

A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | |

A1 |
EI | VLI | LI | SLI | EI | CLI | LI | LI | EI | CLI | LI | VLI |

A2 |
VHI | EI | HI | HI | CHI | EI | HI | SMI | CHI | EI | HI | SMI |

A3 |
HI | LI | EI | SMI | HI | LI | EI | SLI | HI | LI | EI | EI |

A4 |
SMI | LI | SLI | EI | HI | SLI | SMI | EI | VHI | SLI | EI | EI |

CR |
0.091 | 0.07 | 0.051 |

##### Table 18

M3 | E1 | E2 | E3 | |||||||||

A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | |

A1 |
EI | LI | VLI | CLI | EI | VLI | VLI | CLI | EI | LI | VLI | CLI |

A2 |
HI | EI | EI | VLI | VHI | EI | EI | LI | HI | EI | SLI | VLI |

A3 |
VHI | EI | EI | LI | VHI | EI | EI | LI | VHI | SMI | EI | SLI |

A4 |
CHI | VHI | HI | EI | CHI | HI | HI | EI | CHI | VHI | SMI | EI |

CR |
0.096 | 0.09 | 0.079 |

##### Table 19

M4 | E1 | E2 | E3 | |||||||||

A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | |

A1 |
EI | VLI | LI | SLI | EI | VLI | VLI | SLI | EI | LI | SLI | SLI |

A2 |
VHI | EI | EI | HI | VHI | EI | SMI | SMI | HI | EI | SMI | SMI |

A3 |
HI | EI | EI | HI | VHI | SLI | EI | SMI | SMI | SLI | EI | EI |

A4 |
SMI | LI | LI | EI | SMI | SLI | SLI | EI | SMI | SLI | EI | EI |

CR |
0.046 | 0.06 | 0.016 |

##### Table 20

M5 | E1 | E2 | E3 | |||||||||

A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | |

A1 |
EI | VLI | LI | SMI | EI | CLI | VLI | SLI | EI | VLI | VLI | SLI |

A2 |
VHI | EI | SMI | VHI | CHI | EI | EI | HI | VHI | EI | SMI | HI |

A3 |
HI | SLI | EI | HI | VHI | EI | EI | SMI | VHI | SLI | EI | HI |

A4 |
SLI | VLI | LI | EI | SMI | LI | SLI | EI | SMI | LI | LI | EI |

CR |
0.088 | 0.015 | 0.086 |

##### Table 21

M6 | E1 | E2 | E3 | |||||||||

A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | A1 | A2 | A3 | A4 | |

A1 |
EI | CLI | VLI | LI | EI | CLI | VLI | VLI | EI | VLI | LI | LI |

A2 |
CHI | EI | SMI | HI | CHI | EI | SMI | SMI | VHI | EI | SMI | HI |

A3 |
VHI | SLI | EI | SMI | VHI | SLI | EI | SMI | HI | SLI | EI | SMI |

A4 |
HI | LI | SLI | EI | VHI | SLI | SLI | EI | HI | LI | SLI | EI |

CR |
0.065 | 0.093 | 0.093 |

##### Table 22

Goal | DC | O | M |

DC |
$([0.5,0.5],[0.5,0.5])$ | $([0.632,0.697],[0.315,0.373])$ | $([0.35,0.4],[0.6,0.65])$ |

O |
$([0.29,0.363],[0.639,0.71])$ | $([0.5,0.5],[0.5,0.5])$ | $([0.126,0.229],[0.773,0.875])$ |

M |
$([0.6,0.65],[0.35,0.4])$ | $([0.765,0.865],[0.149,0.243])$ | $([0.5,0.5],[0.5,0.5])$ |

##### Table 23

Goal |
DC | O | M | |||

DC |
0 | 0 | 0.2 | 0.307 | −0.232 | −0.152 |

O |
−0.34 | −0.21 | 0 | 0 | −0.668 | −0.449 |

M |
0.152 | 0.232 | 0.434 | 0.645 | 0 | 0 |

##### Table 24

Goal | DC | O | M | |||

DC |
1 | 1 | 1.586 | 2.026 | 0.586 | 0.708 |

O |
0.457 | 0.613 | 1 | 1 | 0.215 | 0.355 |

M |
1.419 | 1.705 | 2.714 | 4.412 | 1 | 1 |

##### Table 26

Main criteria | DC | O | M | ||||||||||||

Weights | 0.316 | 0.167 | 0.517 | ||||||||||||

Sub-criteria | DC1 | DC2 | DC3 | DC4 | DC5 | O1 | O2 | O3 | O4 | M1 | M2 | M3 | M4 | M5 | M6 |

Weights | 0.32 | 0.1 | 0.21 | 0.08 | 0.29 | 0.33 | 0.15 | 0.39 | 0.13 | 0.27 | 0.22 | 0.05 | 0.26 | 0.08 | 0.11 |

Overall | 0.10 | 0.03 | 0.07 | 0.02 | 0.09 | 0.055 | 0.025 | 0.066 | 0.021 | 0.14 | 0.11 | 0.03 | 0.14 | 0.04 | 0.06 |

##### Table 27

DC1 | DC2 | DC3 | DC4 | DC5 | O1 | O2 | O3 | O4 | |

A1 |
0.353 | 0.092 | 0.071 | 0.121 | 0.074 | 0.153 | 0.186 | 0.277 | 0.461 |

A2 |
0.121 | 0.155 | 0.224 | 0.197 | 0.310 | 0.455 | 0.398 | 0.375 | 0.126 |

A3 |
0.202 | 0.190 | 0.219 | 0.308 | 0.374 | 0.318 | 0.343 | 0.250 | 0.237 |

A4 |
0.324 | 0.564 | 0.487 | 0.375 | 0.242 | 0.074 | 0.073 | 0.099 | 0.176 |

M1 | M2 | M3 | M4 | M5 | M6 | |

A1 |
0.080 | 0.091 | 0.069 | 0.127 | 0.109 | 0.079 |

A2 |
0.499 | 0.467 | 0.185 | 0.374 | 0.435 | 0.437 |

A3 |
0.191 | 0.220 | 0.238 | 0.296 | 0.319 | 0.274 |

A4 |
0.230 | 0.222 | 0.508 | 0.202 | 0.136 | 0.210 |

### 5.3 Sensitivity Analysis

*X*-axis represents the change between CHI and CLI of the main criterion weight for four alternatives while

*Y*-axis represents the ranking of alternatives. In this analysis, we change the weights of a certain criterion for each expert between CHI and CLI while the other criteria weights are fixed. For instance, when the weight of

*organizational*criterion with respect to

*digital competence*criterion is changed between CHI and CLI, A2 has always placed in the first rank; when the weight of

*management*criterion with respect to

*organizational*criterion is also changed between CHI and CLI, A2 has been always observed in the first rank similarly. Unlike the others, when the weight of

*management*criterion with respect to

*digital competence*criterion is changed between CHI and CLI, A4 has only placed in the first rank while its weight is CHI and A2 has been observed as the best alternative in other linguistic weights. Sensitivity analysis shows that the main criterion weights only have a limited effect on results and there is not a noteworthy change in the ranking of alternatives.

### 5.4 Comparative Analysis

##### Table 31

Main criteria | DC | O | M | ||||||||||||

Weights | 0.203 | 0.08 | 0.717 | ||||||||||||

Sub-criteria | DC1 | DC2 | DC3 | DC4 | DC5 | O1 | O2 | O3 | O4 | M1 | M2 | M3 | M4 | M5 | M6 |

Weights | 0.64 | 0.018 | 0.08 | 0.01 | 0.26 | 0.299 | 0.07 | 0.57 | 0.06 | 0.299 | 0.18 | 0.03 | 0.37 | 0.05 | 0.06 |

Overall | 0.13 | 0.004 | 0.016 | 0.002 | 0.05 | 0.024 | 0.006 | 0.046 | 0.005 | 0.215 | 0.132 | 0.023 | 0.27 | 0.035 | 0.045 |

##### Table 32

Main criteria | DC | O | M | ||||||||||||

Weights | 0.262 | 0.088 | 0.65 | ||||||||||||

Sub-criteria | DC1 | DC2 | DC3 | DC4 | DC5 | O1 | O2 | O3 | O4 | M1 | M2 | M3 | M4 | M5 | M6 |

Weights | 0.43 | 0.06 | 0.18 | 0.04 | 0.285 | 0.33 | 0.09 | 0.51 | 0.07 | 0.284 | 0.216 | 0.03 | 0.33 | 0.054 | 0.08 |

Overall | 0.11 | 0.02 | 0.047 | 0.01 | 0.075 | 0.03 | 0.008 | 0.045 | 0.006 | 0.185 | 0.14 | 0.02 | 0.217 | 0.035 | 0.054 |

##### Table 33

Alternatives | A1 | A2 | A3 | A4 |

Final Scores | 0.116 | 0.488 | 0.201 | 0.195 |

Rank | 4 | 1 | 2 | 3 |