Virtual reality headsets for employee training in enterprises: fuzzy SRP data-driven framework for a comprehensive evaluation

Abstract 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.