creations. Analysing these models, the following network measures add explanatory power:
closeness centrality, clustering coefficient, out-degree, degree, and weighted degree.
Figure 6
Summary of Findings
This study measures performance by the demand and the price of artists. The USD model is included in this study to demonstrate the difference between the USD and ETH measures and, therefore, to motivate future research on the effect of conversion rates on crypto art prices.
Nonetheless, the following discusses the theoretical contribution of the ETH and bids model results Regarding the other two models, Closeness centrality has a significantly negative effect on the ETH performance of an artist. This finding supports prior literature on the bridging and bonding discussion. In the particular context of this transaction network, shorter distances between artists negatively affect ETH value. This could potentially reflect a homogeneous information flow in these circles and is in line with previously discussed findings of the bridging and bonding discussions (Burt, 1983; Burt, 2009; Uzzi & Spiro, 2005; Wachs et al., 2018).
Similarly, the clustering coefficient shows a negative effect on the demand for an artist.
This indicates that a higher density in the ego network of an artist's peer transactions decreases the number of bids placed on his works. However, it is not clear why the density of an artist's ego network hurts demand but not price. Network density is found to have a negative effect on innovation performance (Tan et al., 2015) as well as the price of cryptocurrencies (Motamed
& Bahrak, 2019). In the context of this study, the higher the density of an ego network, the fewer bids are placed.
Regarding betweenness centrality, the variable did not qualify as a value-adding variable within our data set. This finding is most likely due to the abnormal distribution of this measure.
However, due to the significantly negative results for the effect of clustering and closeness, this study confirms 2 out of 3 hypotheses testing for briding vs bonding positions. Future research is required to explore the occupation of structural holes by artists in crypto networks to confirm or reject this proposition. To conclude, the present findings support the bridging side of the debate as closeness centrality and the clustering coefficient significantly negatively affect performance.
Therefore, Hypothesis 1 of this study is partially supported.
Overall, to answer the sub-question whether the relationship of our measures with performance differs among performance metrics: Yes. The difference in these findings may be due to a high price being more reflective of access to information than high demand. Therefore, high closeness centrality leads to access to similar information and resources, reflecting negatively on an artist's performance. However, suppose the creativity of an artist has no significant relationship with the fame of an artist (Banerjee & Ingram, 2018). In that case, the nature of the information available to artists with high betweenness centralities may be the source of competitive advantage
Regarding the signalling theory, the following text addresses the findings for Hypothesis 2 and 3. In the context of signalling theory, the sender of a signal would be the artist (insider) who buys a token. At the same time, the receivers of the message include all participants of the market.
However, the decoding of a signal depends on the environment of the receiver. When an artist decides on how much money to spend on a peer's token or how many peers to interact with, he/she sends a conscious or unconscious signal to consumers and peers.
The degree measures of this study capture the effect of artists' peer-trades in terms of the count and weight of interactions. The out-degree has a significantly positive effect on an artist's demand. Therefore, the more tokens an artist sells to his peers, the more tokens he sells overall.
What is interesting here is that the in-degree measure did not add significant power to either model.
Therefore, buying a peer's work does not have a significant impact on performance. Similarly, the amount of money spent on a peer's works (weighted in-degree) has no impact on performance.
Interestingly, the amount of money peers spent on an artist's works (weighted out-degree) also has no significant effect. This could hint at artists engaging in peer-trade to drive up value due to multiple changes in ownership of a token (Baily, 2019). If the monetary value of the peer- transaction is not relevant to the demand of an artist. However, the count of peer interactions (degree) is relevant to the demand and ETH value. This could indicate a signalling effect of peer trades.
In that sense, artists would engage in peer trading because they understand that other artists are visible owners of their works, which drives up the price and demand. Though the findings for the in-degree measure were not significant, there is reason to believe that there is some form of mutual understanding among artists that if they support another, it reflects positively on their performance. This argument is in line with the concept of coopetition, which describes a strategical
approach to competition that includes cooperation. Coopetition is a concept that applies to the competitive dynamics in the creative industries, blockchain, and social media (Virtanen et al., 2017). Coopetition is a common phenomenon in the creative industries and at the heart of the philosophy for circular economies (Narayan & Tidström, 2020; Walley, 2007; Yami et al., 2010). In that sense, artists' competition in crypto art markets invites future research to understand the strategies artists employ to signal legitimacy.
Finally, the weighted degree within the peer network has a positive effect on the ETH performance of an artist. This shows that the amount of money spent between artists has a positive effect on the ETH price of an artist. This is in line with the signalling argument because artists likely understand that buyers would perceive these works as more valuable if they spent more significant amounts on each other's works. Thereby, peers perform the market selection. This nature of the peer selection systems employed in crypto art markets aligns with findings by Franceschet and Colavizza (2019). They analyse the curation activities of artists in crypto spaces.
The following elaborates on the implications for practitioners.
To conclude, the findings of this study find support for positive effects of degree, out-degree, and weighted degree. When translating the SNA measures back to the form of interaction they are based on, the total number of trades an artist has with his peers, the number of sales to peers and the economic magnitude of total peer transactions partially explain the success of an artist on SuperRare. Interestingly, the economic magnitude of the transaction is not significant for the number of bids placed on an artist. This could hint at artist's engaging in low-cost transactions with each other to drive up their demand. Many bids may, however, be associated with the conversion rate at the time of the auction. Therefore, this divergence in results for the performance
signals sent through peer trading on performance is encouraged. Moreover, the intentions of an artist engaging in peer trades requires qualitative research.
After assessing the statistical data through the two theoretical lenses of this paper, we can answer to what extent peer transactions influence performance. The first theoretical lens provides insights with whom to interact, while the second lens provides insights on how to interact, i.e., performing many vs large trades. This research concludes that the effects differ among the metrics of price and demand. Artists should engage with many peers across diverse networks to optimise their network position. Further, artists or managers need to consider whether they seek buzz or high prices. This study suggests that transactions do not require much weight to create buzz, but the count of peer trades is relevant to artists’ demand.
The research question has, therefore, only been answered to an extent. Nevertheless, the study demonstrates an example of performing SNA on crypto art ledger data and motivates researchers to further participate in the extension of social capital literature to the fields of crypto art markets. Before elaborating on these channels for future research, the following text discusses the implication for practitioners.
5.2 Implications for Practitioners
Unfortunately, there seems to be no recipe for success in creative industries.
Nobody knows for which art there will be demand (Caves, 2000); however, in the context of crypto art, the speculations of artistic value performed by the market influence the value of crypto artists (Bailey, 2017). This study reports findings that help artists navigate through the crypto world as entrepreneurs. In that sense, the results of this study encourage the artists to engage in peer trading by creating many lose ties with multiple peer networks. Moreover, in doing so, the focus should lay on the identity of the peers one trades with and the accompanying signal to
consumers, rather than the amount of money spent on a peer's work. Additionally, the management of crypto art platforms is encouraged to analyse and understand the communities that form between artists and find inspiration for marketing activities and services accordingly.
5.3 Limitations & Future Research
The limitations of this study are diverse and extensive. Starting with the regression findings, the results are to be interpreted with caution as many unknown factors influence the price and demand of artists other than network position within the peer network, which is only based on transactions. Furthermore, the knowledge transfer of inter-artist contact likely manifests in social rather than financial transactions. Therefore, we encourage future researchers to investigate the effect of network position in inter-artist networks in diverse empirical settings. For example, the linear modelling of performance in crypto art networks presented here could be extended by network-based semantics and non-financial interactions of leading crypto platforms like Twitter or Telegram. This could allow for a deeper understanding of inter-peer interactions and the network positions of artists within a peer network that is not only based on transactions. In that sense, as this paper bases the results on data from a single crypto art platform, to better understand the crypto art community, future research is encouraged to study a larger sample of crypto art platforms and the inter-platform connections of peer as well as the on-site interactions. In this context, the signals originating from other interactions than transactions yield exciting directions for future research.
Moreover, the transaction graph constructed in this study is relatively simple compared to the complex graph approach presented in recent literature on ETH network analysis (Lin et al., 2020). Applying complex network approaches to crypto art networks has not been done to date
USD measures is not explained within the scope of this study. Finally, Crypto art elevates a conventional art investment to a financial asset traded on ETH without the apparent influence of any third parties or intermediaries. Therefore, in addition to the cultural and economic value of non-crypto art, crypto art entails a financial return associated with the conversion rate of ETH.
Therefore, the insiders/selectors in crypto art markets signal the value of artwork and signal financial market information. This means that artists may be perceived as having access to insider information on the future performance of other artists or the ETH market. Therefore, researching the identities of (rate-oriented) traders and observing how their behaviour impacts an artists' performance would improve the understanding of how value is created in crypto art networks. In that sense, we encourage studying tokens as financial assets and the bidding behaviour of crypto art consumers.
In general, the social interaction of peers in the digital art markets needs further attention.
The physical place of interaction used to add much value to inter-artist interactions. Therefore, understanding the nature of online interactions between artist and their effect on performance is crucial for the future of management in the creative industries. This research can be extended to other areas in creative industries than crypto art. Moreover, identifying and analysing digital art communities or artistic movements over time could substantially improve performance modelling.
On that note, network and performance measures – distinct from those presented here – are encouraged for future research in the field.