A Data-Driven MCDM Approach-Based Spherical Fuzzy Sets for Evaluating Global Augmented Reality Providers in Education - Phi-Hung Nguyen (IEEE Access)
Amidst the heightened popularity of augmented reality (AR), particularly in education driven by the transformative impact of the COVID-19 pandemic on remote learning, this study addresses the intricacies surrounding the discerning selection of AR providers for educational purposes. It introduces a comprehensive decision support model, utilizing Spherical Fuzzy Sets (SFSs) to enhance the decision-making process. The methodology involves the application of SF-Delphi to ascertain the relative significance of eight pivotal criteria shaping AR provider selection. The Spherical Weighted Arithmetic Mean (SWAM) operator ensures an exhaustive and unbiased assessment. Subsequently, the Technique for Order of Preference by Similarity to the Ideal Solution (SF-TOPSIS) is systematically applied to evaluate and rank ten global AR providers. This research yields a dual contribution. Firstly, the proposed decision support model systematically guides AR provider selection, equipping decision-makers to navigate the intricacies of AR technology choices efficiently. Additionally, the impact extends to education, enriching the understanding of AR applications. This knowledge promotes well-informed and innovative AR technology utilization, enhancing the overall learning experience. In conclusion, this study introduces a pioneering decision support model that leverages SFSs to guide the selection of educational AR providers. This structured framework empowers decision-makers and advances the comprehension of AR dynamics, further propelling the strategic integration of AR technology in education.