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Metaverse technology has attracted much attention in many contexts, including industry, education, marketing and business. Some recent studies have focused on qualitative studies based on the actual definition of the metaverse. However, practical research related to metaverse platforms remains in its infancy. This study provides actionable insights into the determinants of metaverse adoption by using perceived fluidity.

A two-stage structural equation modeling (SEM) approach and Hayes’ Macro approach are used to examine the proposed hypotheses.

Results show that technology features (e.g. real-time rendering, interactivity and immersion) increase users’ perceived fluidity, which in turn leads to positive intentions to use the metaverse. A high level of perceived realism is not an advantage for metaverse technology and plays a negative moderating role in this mechanism. The interaction of awe with technological features can enhance the negative moderating effects of realism.

This study pioneers the examination of perceived fluidity as a key determinant of metaverse adoption, offering a novel perspective beyond traditional factors. It uniquely identifies the paradoxical role of perceived realism, demonstrating its potential negative impact on user experience. In addition, the research highlights the reinforcing effect of awe on this relationship.

Selecting an appropriate strategy can significantly influence an organization’s direction, but numerous interrelated factors make this decision complex and uncertain. Identifying these relationships and making informed decisions often involves navigating uncertainty. This paper aims to assist managers in leveraging the organization’s intellectual assets to select the most suitable knowledge management strategy.

This research proposes fuzzy cognitive maps to model and select the appropriate strategy. Given that knowledge is a company’s most valuable asset and its management is critical, the proposed method for selecting a knowledge management strategy was tested in one of Iran’s largest logistics companies. The factors influencing strategy adoption were treated as concepts in the fuzzy cognitive network, with evaluation criteria applied to three strategies: human-oriented, system-oriented and dynamic.

The model’s output identifies the most suitable strategy based on knowledge management principles and the specific characteristics of the target project within the organization. Compared to the most cited paper in this field, the proposed model shows a 0.15% improvement in sensitivity and a 0.22% improvement in separation degree.

This study proposes a novel method using fuzzy cognitive maps to assist organizations in selecting the best knowledge management strategy, even amidst multiple influencing factors and uncertain conditions.