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To examine the effects of the metaverse on firms’ marketing activities.

A conceptual paper.

It provides evidence of the growing importance of different value capture mechanisms in the metaverse.

Among the first articles on this topic.

This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.

This paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.

Results show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.

This paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.

This paper aims to investigate the drivers of foreign direct divestment (FDD), how it relates to foreign direct investment (FDI) flows and stocks and its implications for developing countries. While divestment occurs for various reasons, it can be explained by reversing the propositions implied by FDI theories.

The authors combine FDI data and FDI theories to provide theoretical explanations for FDD and what it means for developing countries. FDI stock and flow data are used to derive inferences on trends in FDD and examine the implications of FDI theories on FDD.

Changes in the modes of global production and the rise of COVID-19 have reinforced the trend of stagnant or diminishing FDI flows observed since the global financial crisis, with implications for FDD. The authors demonstrate how the various FDI theories can be used to explain FDD, except for the currency areas hypothesis. By reviewing the costs and benefits of FDI, it is concluded that shrinking FDI flows and stocks may not be as detrimental for developing economies as it is typically portrayed.

The paper uses two original approaches to measure and explain the motives for FDD. The first is a reassessment of FDI theories in a way that makes them valid theories for FDD. The second original approach is to interpret data on FDI flows and stocks to imply the trends governing FDD, which is useful, as data on foreign divestment are not available on a country or regional basis.