Lastly, the cultural aspect in developing economies has been found to hamper technological transitions [9]. In developing countries, the relatively low socio-economic status of people makes them more accustomed to outdated pedagogical methods [10], which can impede crucial transitions towards EDUC4. For the developing economies, [PERSON] et al. [11] underlined the necessary alignment of the strategies for implementing EDUC4 not just in infrastructure for universities but also in the inherent incapability of the students and parents to provide the needed technology at home. To this end, a recent work reported by [PERSON] et al. [10] comprehensively identifies a set of 12 barriers to implementing EDUC4 among higher education institutions (HEIs) in developing economies through a systematic literature review. They provided essential insights into how these barriers interplay within HEIs, particularly in developing economies, and offered possible interlinkages among these barriers to aid educational leaders in carrying out crucial decision-making processes. This work intends to respond to their call to identify priority EDUC4 implementation barriers. Narrowing the focus to those priority barriers is necessary for planning, resource allocation decisions, and strategy development for the effective, efficient, and sustainable transition to EDUC4. To this end, three research questions are highlighted in this work: (1) How can EDUC4 barriers be prioritized while accounting for their interrelationships? (2) What strategies can be employed to address priority EDUC4 barriers? (3) What are the effects of addressing priority EDUC4 barriers? Building on the previous work of [PERSON] et al. [10], an expert elicitation-based analytic approach using the decision-making trial and evaluation laboratory (DEMATEL) [12; 13] is adopted in this work. The DEMATEL is used to evaluate the intertwined causal relationships among EDUC4 barriers. The output of the DEMATEL is a subset of barriers that are crucial for EDUC4 implementation. However, the expert elicitation inputs of the DEMATEL are subject to epistemic uncertainties, which may yield counterintuitive insights when not addressed. While various extensions of the DEMATEL were introduced to address the uncertainties in eliciting judgments, this work integrates the notion of Fermatean fuzzy sets (FFS) [14] into the DEMATEL analysis, along with the maximum mean de-entropy (MMDE) algorithm [15] to discriminate less critical EDUC4 barriers from the most critical ones. The use of the FFS provided an expanded space of membership grades, which offers a platform for capturing a higher degree of uncertainty. A case study in Philippine universities demonstrates the application of such an approach and offers some strategic insights that can be extended to other developing economies. Aside from introducing the Fermatean fuzzy DEMATEL, which formed the methodological contribution of this work, the main departure of this work is in evaluating the barriers of implementing EDUC4, effectively contributing to the domain literature in gaining crucial insights not just for Philippine universities but for comparable HEIs in other developing regions. The remainder of this paper is arranged as follows: Section 2 provides a brief literature review about EDUC4 and the background of the methodology adopted in this work, while Section 3 details some preliminaries to FFS, the DEMATEL, and the MDDE algorithm, and details the proposed approach for prioritizing EDUC4 barriers, along with the details of the post-analysis employed to analyze the results further. The case in the Philippines is adopted as the specific environment of this work. Discussions of the findings are offered in Section 4, which ends with some concluding remarks and future works in Section 5.