## 2 Literature Review
In addition, as an input to resource allocation decisions, determining the degree of relevance of these barriers would help HEIs efficiently transition to an environment that facilitates EDUC4. To this end, the DEMATEL is employed in this work. The DEMATEL, developed by Gabus and Fontela [12] and [PERSON] [13], is a tool based on graph theory that considers the set of elements (e.g., barriers) as vertices and the causal relationships between elements as directed edges. Based on the direct relationships among elements elicited by expert decision-makers, the DEMATEL attempts to achieve two objectives: (1) examining the total relationships among elements brought about by indirect (transitive) relationships; and (2) clustering these barriers into a net cause or net effect. These objectives provide powerful decision support in analyzing complex problems that humans can hardly understand. Due to its straightforward computational characteristics, the DEMATEL is used in various domain applications [39]. Some applications include some domains in education, such as sustainability in management education [40], performance evaluation [41], mobile learning [42], and e-learning in the COVID-19 pandemic [43], among others. [PERSON] et al. [44] put forward a review of its applications. Due to the uncertainty inherent in eliciting judgments within the DEMATEL, the integration of modelling approaches that capture fuzziness has become a popular approach. The introduction of fuzzy set theory [45] revolutionized an array of analytic tools that address vagueness and uncertainty in computing information. The fuzzy set extension of DEMATEL was popularized by [PERSON] and [PERSON] [46] and colleagues and its application has been widespread, even in handling recent issues in the education sector; e.g., [PERSON] and [PERSON] [47], [PERSON] et al. [48], [PERSON] et al. [49]. With the limitation of the fuzzy set theory in better handling uncertainty by introducing non-membership functions, the development of fuzzy environments associated with intuitionistic fuzzy sets [50], Pythagorean fuzzy sets [51], and Fermatean fuzzy sets [14] forms a class of \(q\)-rung orthopair fuzzy sets [52]. Following these developments, computational platforms based on the DEMATEL were offered, including some variations of intuitionistic fuzzy DEMATEL [53; 54; 55] and Pythagorean fuzzy sets [56; 57]. While the Pythagorean fuzzy sets offer a distinct advantage over the intuitionistic fuzzy sets and [PERSON]'s fuzzy sets, expert decision-makers eliciting judgments within the DEMATEL may be limited to express uncertainty under a few conditions. Thus, the introduction of FFS is deemed more applicable in handling such conditions. Despite its advantages, current literature has not offered a computational platform in integrating FFS within the DEMATEL. This work addresses such a gap by developing a Fermatean fuzzy DEMATEL model. In addition, following the required systematic approach for computing threshold values that would identify significant directed relationships among elements in the DEMATEL, the MMDE algorithm proposed by [PERSON] and [PERSON] [15] is adopted in this work. The MMDE algorithm applies the concept of entropy to identify the adequate information of the influence matrix and draws a threshold to filter the unnecessary information in the influence matrix [15]. Entropy is a physical measurement of thermal dynamics and has become an essential concept in the social sciences [58; 59]. In information theory, entropy is used to measure the expected information content of specific messages and is a criterion for the amount of \"uncertainty\" represented by a discrete probability distribution. Entropy is affected by the probabilities of the elements in a system. The larger the entropy, the more the uncertainty of single events, which implies that the system is more unstable.