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Criminals have quickly discovered the advantage of crypto assets, with its pseudo-anonymity, untraceability and the ability to freely exchange crypto assets across borders, which makes it an ideal tool for money laundering activities. Switzerland has a technology-neutral framework, and crypto assets are regulated by the existing anti-money laundering (AML) legislation. The purpose of this paper is to gain insights into the industry adoption of measurements to prevent money laundering through crypto assets and if they are compliant with national and international AML regulations.

Semi-structured expert interviews were conducted with participants having expertise in compliance, AML and crypto assets with focus on Switzerland. The interviews were analyzed using the thematic analysis.

The experts have a general consensus that Switzerland is a pioneer when it comes to regulating crypto assets. It is perceived that legislations are released without industry consultation and that AML processes for fiat transactions also work for crypto assets, which is not the case. The results show that the industry wants a consortium to fight money laundering in crypto assets in Switzerland. The current measures to identify money laundering are not optimal, yet, it is the best solution and according to national and international regulations the businesses are perceived to be compliant.

This paper offers new insights on the challenges of AML regulations in crypto assets, given the limited information available. It also provides good practice examples for addressing these challenges, benefiting policymakers, regulators and practitioners in the crypto asset ecosystem.

The study aims to explore the digital fashion trend within the Metaverse, characterized by non-fungible tokens (NFTs), across Twitter networks. Integrating theories of diffusion of innovation, two-step flow of communication and self-efficacy, the authors aimed to uncover the diffusion structure and the influencer's social roles undertaken by social entities in fostering communication and collaboration for the advancement of Metaverse fashion.

Social network analysis examined the critical graph metrics to profile, visualize, and cluster the unstructured network data. The authors used the NodeXL program to analyze two hashtag keyword networks, “#metaverse fashion” and “#metawear,” using Twitter API data. Cluster, semantic, and time series analyses were performed to visualize the contents and contexts of communication and collaboration in the diffusion of Metaverse fashion.

The results unraveled the “broadcast network” structure and the influencers' social roles of opinion leaders and market mavens within Twitter's “#metaverse fashion” diffusion. The roles of innovators and early adopters among influencers were comparable in collaborating within the competition venues, promoting awareness and participation in digital fashion diffusion during specific “fad” periods, particularly when digital fashion NFTs and cryptocurrencies became intertwined with the competition in the Metaverse.

The study contributed to theory building by integrating three theories, emphasizing effective communication and collaboration among influencers, organizations, and competition venues in broadcasting digital fashion within shared networks. The validation of multi-faceted Social Network Analysis was crucial for timely insights, highlighting the critical digital fashion equity in capturing consumers' attention and driving engagement and ownership of Metaverse fashion.

This paper aims to establish a fundamental and comprehensive understanding of non-fungible tokens (NFTs) by identifying and structuring common characteristics within a taxonomy. NFTs are hyped and increasingly marketed as essential building blocks of the Metaverse. However, the dynamic evolution of the NFT space has posed challenges for those seeking to develop a deep and comprehensive understanding of NFTs, their features and their capabilities.

Utilizing common guidelines for the creation of taxonomies, the authors developed (over 3 iterations), a multi-layer taxonomy based on workshops and interviews with 11 academic and 15 industry experts. Through an evaluation of 25 NFTs, the authors demonstrate the usefulness of the taxonomy.

The taxonomy has 4 layers, 14 dimensions and 42 characteristics, which describe NFTs in terms of reference object, token properties, token distribution and realizable value.

The authors' framework is the first to systematically cover the emerging NFT phenomenon. This framework is concise yet extendible and presents many avenues for future research in a plethora of disciplines. The characteristics identified in the authors' taxonomy are useful for NFT- and Metaverse-related research in finance, marketing, law and information systems. Additionally, the taxonomy can serve as an information source for policymakers as they consider NFT regulation.