Fermatean Fuzzy DEMATEL and MMDE Algorithm for Modelling the Barriers of Implementing Education 4.0: Insights from the Philippines

Technological transitions in the education sector of developing economies are faced with a range of barriers, involving resource scarcity, socio-cultural concerns, and issues related to management and policy. The popularity of Industry 4.0 has prompted Education 4.0 (EDUC4), an approach to learning that involves transformation using advanced technologies. While a recent work reported a comprehensive list of barriers to EDUC4 implementation, particularly in developing economies, further analysis to identify those priority barriers remains a gap. Thus, this work addresses this gap by introducing a novel methodological extension of the decision-making trial and evaluation laboratory (DEMATEL) method following the integration of Fermatean fuzzy sets (FFS). The FFS, compared to other fuzzy environments, could capture higher levels of uncertainties that are associated when eliciting judgments necessary for the DEMATEL. Such integration is aided by the maximum mean de-entropy (MMDE) algorithm, which analytically determines the threshold value crucial for constructing the prominence-relation map of the DEMATEL. Following its application in evaluating the implementation of EDUC4 in Philippine universities, the critical barriers are the lack of training resources, costs, insufficiency of available technologies, skills gap of human resources, knowledge gap, and the complexity of the learning platforms. Among this set, barriers related to cost and lack of training resources are deemed the most prominent ones. The statistical test on the impact of addressing the two prominent barriers shows that addressing the barrier related to costs yields statistically more favorable results regarding the mitigation of other EDUC4 implementation barriers. Although these insights may contain idiosyncrasies, they can serve as starting points of discussion in other relevant developing economies. These methodological and practical contributions advance the development of analytical tools under uncertainty that can handle pressing problems such as the EDUC4 implementation.