Virtual reality (VR) is progressively transforming employee training in companies by offering immersive and engaging learning experiences. Nevertheless, the selection of an appropriate VR headset is vital for optimizing training effectiveness. This paper addresses this issue by proposing a novel hybrid fuzzy multi-criteria decision-making model that integrates the improved fuzzy stepwise weight assessment ratio analysis (IF-SWARA) with the fuzzy simple ranking process (F-SRP). The IF-SWARA methodology is employed to compute the relative weights of the selection criteria for VR headsets utilized in employee training, whereas the newly developed F-SRP is implemented to rank the various VR headsets. By employing the IF-SWARA method, the model offers a more nuanced understanding of criteria weights, thereby reflecting the differing significance of various headset features. The research’s novelties and contributions are as follows: (1) This study is the first to select VR headsets by applying multi-criteria methods. (2) The F-SRP model is developed for the first time in the literature. (3) The introduced F-SRP methodology allows for a comprehensive ranking of the available VR headsets, facilitating informed decision-making. The paramount indicators for selecting VR headset options for training in enterprises consist of technical specifications, comfort and ergonomics, and screen specifications. The results obtained from the fuzzy SRP indicate that the Apple Vision Pro surpasses the other alternatives. Finally, the robustness and applicability of the proposed model are evaluated through an exhaustive sensitivity analysis. This research possesses broader implications for VR training in enterprises by providing a robust and reliable framework, ultimately contributing to the development of more effective and impactful VR training programs.

Storing hydrogen resources underground can accelerate the transition to renewable energy, facilitate energy supply security, and the adoption and expansion of hydrogen energy, a clean energy source. The selection of sustainable underground hydrogen storage systems is a critical research topic for addressing environmental issues caused using fossil fuels. However, decision-makers still lack a consensus-based and sustainability-oriented framework that can comparatively evaluate alternative underground hydrogen storage geological formations under economic, environmental, social, and technical uncertainties, which constitutes a critical barrier to large-scale hydrogen deployment. This issue has become more prominent as fossil-based fuel reserves are gradually decreasing worldwide. In contrast, researchers and practitioners lack a consensus on which underground storage method is most suitable for economical, safe, and efficient hydrogen storage. If this problem is not addressed correctly and reasonable solutions are not obtained, continued dependence on fossil fuels may persist. Alternatively, other renewable energy sources with relatively lower efficiency and performance may be adopted. In both cases, significant delays in achieving the global sustainability goal are likely to occur. We propose an integrated fuzzy decision-making framework (F-WENSLO & Dombi-Bonferroni & F-ARTASI) to address this selection problem under uncertainty. The proposed framework integrates fuzzy WENSLO (Weights by ENvelope and SLOpe) for robust sustainability-based criteria weighting, the Dombi–Bonferroni aggregation operator to model interdependencies among criteria explicitly, and the fuzzy ARTASI (Alternative Ranking Technique based on Adaptive Standardized Intervals) method to provide flexible and stable ranking of geological alternatives beyond rigid distance-based approaches. Key advantages of the proposed model include producing reliable and consistent solutions that accurately reflect real-world conditions for selecting sustainable underground hydrogen storage systems. The results revealed that C14 (job creation and employment opportunities) (0.0603) is the most influential criterion in selecting the most suitable storage system. In addition, salt caverns with an Ωi of 10,5167 have achieved the highest score, placing them in the first position, and it is the most suitable and advantageous underground hydrogen storage option. The suggested decision-making tool can yield reliable and robust solutions in real-world conditions, enabling the planning of infrastructure design for hydrogen energy systems that incorporate sustainability dimensions. In that regard, the developed model possesses the characteristics of an efficient and practical roadmap that can guide policymakers and decision-makers in transitioning from fossil-based energy sources to renewable energy sources. It has been implemented to evaluate underground geological formations that could facilitate the storage of hydrogen energy underground, serving as a case study. The reliability and robustness of this tool have been verified through extensive validation tests.

The primary objective of this study is to develop a structured digital transformation strategy roadmap that independent audit firms can utilize to manage digital transformation processes effectively. Digital transformation extends beyond integrating Industry 4.0 and advanced technologies into business operations. It necessitates restructuring business models, decision-making frameworks, and stakeholder communication mechanisms. Its implications are critical across all industries. In independent auditing, ensuring data accuracy, enhancing audit process transparency, and meeting speed and quality requirements are becoming increasingly vital. Digital transformation addresses these needs and provides independent audit firms with a sustainable competitive advantage. A review of the existing literature reveals a significant research gap in the identification and prioritization of digital transformation strategies, as well as a lack of comprehensive theoretical studies examining the digital transformation practices of enterprises. This study proposes an integrated decision-making model to address these research and theoretical shortcomings. According to the study results, "providing in-depth analysis with big data analytics and artificial intelligence solutions" is the most essential strategy for managing digital transformation processes. Regarding the applicability of this strategy, "agility" is defined as the most critical and practical criterion. Robustness checks confirm the model's validity and consistency.