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The metaverse has garnered increasing attention from researchers and practitioners, yet numerous firms remain hesitant to invest in it due to ongoing debates about its potential financial benefits. Therefore, it is crucial to analyze how the implementation of metaverse initiatives affects firms’ stock market value – an area that remains underexplored in the existing literature. Additionally, there is a significant lack of research on the contingency factors that shape the stock market reaction, leaving a noticeable gap in managerial guidance on the timing and benefits of investments in the metaverse. To narrow these gaps, we examine whether and when the implementation of metaverse initiatives enhances firms’ stock market value.

Based on 73 metaverse implementation announcements disclosed by Chinese listed firms during January 2021–August 2023, we employ an event study approach to test the hypotheses.

We find that metaverse implementation announcements elicit a positive stock market reaction. Moreover, the stock market reaction is stronger for technology-focused announcements and smaller firms, or when public attention to the metaverse is higher. Nevertheless, firms’ growth prospects do not significantly alter the stock market reaction.

This study extends the nascent literature on the metaverse by applying signaling theory to offer novel insights into the signaling effect of metaverse implementation announcements on stock market value and the boundary conditions under which the effectiveness of the signal varies. Besides, it provides managers with important implications regarding how to tailor the investment and information disclosure strategies of the metaverse to more effectively enhance firms’ stock market value.

Developing severe load spectrum of transport aircraft structures is crucial for enhancing the fatigue damage correlation between full-scale fatigue testing results and operational service. The lack of consensus on severe spectrum development methods for transport aircraft has prompted the current research, resulting in a proposed approach for a severe gust load spectrum based on the acceleration cumulative exceedance surface.

The measured load data were analyzed using a model based on the cumulative exceedance number surface to describe the variation in exceedance numbers. An improved sampling method based on multivariate Markov Chain Monte Carlo was employed to obtain the fleet fatigue damage distribution, enabling the determination of the severity of severe spectrum and the corresponding cumulative exceedance number surface, and a severe gust load spectrum was developed based on the surface.

The method that characterizes load spectrum variation using the cumulative exceedance surface minimizes the randomness of peak-trough pairs by incorporating the correlation of load spectrum peaks and troughs. This approach reduces the variation in fleet fatigue damage, thereby lowering the requirements for the severity of severe spectrum fatigue damage.

The proposed methodology extends from a one-dimensional curve to a two-dimensional surface, accounting for the correlation between peak and trough values to develop a severe spectrum. This approach more accurately describes the variation in acceleration cumulative exceedance numbers, directly benefiting fatigue damage calculation. This study provides valuable references for developing severe spectrum for transport aircraft.

The aim of this study is to empirically investigate the impact of marketing analytics capability on business performance from the perspective of RBV theory.

This study used a survey method to gather information from 225 food processing SMEs registered with the Ghana Enterprise Agency (GEA) in Ghana’s eastern region. A structural equation modeling (SEM) path analysis was used to assess the impact of marketing analytics capability (MAC) on the performance of SMEs.

The results of the study show that MAC significantly and positively affect the financial performance (FP), customer performance (CF), internal business process performance (IBPP) and learning and growth performance (LGP) of Ghanaian SMEs. The findings of this study also illustrated the significance of MAC determinants, including marketing analytics skills (MAS), data resource management (DRM) and data processing capabilities (DPC), in achieving SME success in Ghana.

The research’s conclusions give RBV theory strong credence. The results of this study also provide credence to previous research finding that SMEs should view MAC and its determinants (i.e. DRM, DPC, MAS) as a crucial strategic capability to improve their performance (i.e. FP, CF, IBPP, LGP). With regard to its contribution, this study broadens the body of knowledge on MAC and SME performance, particularly in the context of an emerging economy.