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Metaverse is one of the decade’s most exciting and transformative technological innovations. While the metaverse holds immense promise, it has potential risks and dark sides. This research aims to investigate and identify the crucial dark dimensions associated with the metaverse platforms.

Employing a qualitative phenomenological methodology, the authors interviewed 45 metaverse users to unravel dark dimensions related to the metaverse. Analyzing the themes extracted from the participants' insights revealed an alignment with the underpinnings of the Technology Threat Avoidance (TTA) theory.

The findings of this study revealed seven major dark dimensions: addiction and dependency, isolation and loneliness, mental health issues, privacy and security, cyberbullying and harassment, digital identity theft and financial exploitation.

The study helps organizations and metaverse platforms understand the crucial dark dimensions of the metaverse. This study concludes by synthesizing prevalent themes and proposing propositions, offering insights for practical application and policy considerations.

This study provides a deeper understanding of the dark side of the metaverse environment from a user perspective using the underpinnings of TTA theory.

Artificial intelligence (AI) is a powerful and promising technology that can foster the performance, and competitiveness of micro, small and medium enterprises (MSMEs). However, the adoption of AI among MSMEs is still low and slow, especially in developing countries like Jordan. This study aims to explore the elements that influence the intention to adopt AI among MSMEs in Jordan and examines the roles of firm innovativeness and government support within the context.

The study develops a conceptual framework based on the integration of the technology acceptance model, the resource-based view, the uncertainty reduction theory and the communication privacy management. Using partial least squares structural equation modeling – through AMOS and R studio – and the importance–performance map analysis techniques, the responses of 471 MSME founders were analyzed.

The findings reveal that perceived usefulness, perceived ease of use and facilitating conditions are significant drivers of AI adoption, while perceived risks act as a barrier. AI autonomy positively influences both firm innovativeness and AI adoption intention. Firm innovativeness mediates the relationship between AI autonomy and AI adoption intention, and government support moderates the relationship between facilitating conditions and AI adoption intention.

The findings provide valuable insights for policy formulation and strategy development aimed at promoting AI adoption among MSMEs. They highlight the need to address perceived risks and enhance facilitating conditions and underscore the potential of AI autonomy and firm innovativeness as drivers of AI adoption. The study also emphasizes the role of government support in fostering a conducive environment for AI adoption.

As in many emerging nations, the AI adoption research for MSMEs in Jordan (which constitute 99.5% of businesses), is under-researched. In addition, the study adds value to the entrepreneurship literature and integrates four theories to explore other significant factors such as firm innovativeness and AI autonomy.