PDF (4 MB)
Collect
Submit Manuscript
Open Access

Gathering for Free: Embedding Economic Incentives in Social Networks Shape the Diffusion of NFTs

School of Journalism and Communication, University of Chinese Academy of Social Sciences, Beijing 100102, China
College of Journalism and Communication, Jinan University, Jinan 510632, China
Institute of Journalism and Communication, Chinese Academy of Social Sciences, Beijing 100102, China. He is also with the School of Journalism and Communication, University of the Chinese Academy of Social Sciences, Beijing 100732, China
Show Author Information

Abstract

The digital innovation accompanied by explicit economic incentives have fundamentally changed the process of innovation diffusion. As a representative of digital innovation, NFTs (Non-Fungible Tokens) potentially offer new revenue streams in the digital space. However, current researches mainly focus on transaction networks and community culture, leaving the interplay among diffusion dynamics, economic dynamics, and social constraints on Twitter. By collecting and analyzing NFTs-related tweet dataset, the motivations of retweeters, the information mechanisms behind emojis, and the networked-based diffusion dynamics is systematically investigated. Results indicate that Retweeting is fueled by Freemint and trading information, with the higher economic incentives as a major motivation and some potential organizational tendencies. The diffusion of NFTs is primarily driven by a “Ringed-layered” information mechanism involving individual promoters and speculators. The presentation of content contribute positively to the growth of the retweet network. This study contributes to the innovation diffusion theory with economic incentives embedded.

References

[1]
J. Fagerberg, D. Mowery, and R. Nelson, The Oxford Handbook of Innovation, Oxford university press, 2006.
[2]
J. van Dijck, The Culture of Connectivity: A Critical History of Social Media. Oxford; New York: Oxford University Press, 2013.
[3]
NFTSCAN, Overview of the chain, https://www.nftscan.com/chain/overview, 2023.
[4]
D. Guttentag and S. L. J. Smith, The diffusion of Airbnb: a comparative look at earlier adopters, later adopters, and non-adopters, Curr. Issues Tour., vol. 25, no. 20, pp. 3225–3244, 2022.
[5]
E. M. Rogers, Diffusion of Innovations, 5th Edition, Simon and Schuster, 2003.
[6]
R. Peres, E. Muller, and V. Mahajan, Innovation diffusion and new product growth models: A critical review and research directions, Int. J. Res. Mark., vol. 27, no. 2, pp. 91–106, 2010.
[7]
P. Mukherjee, How chilling are network externalities? The role of network structure, Int. J. Res. Mark., vol. 31, no. 4, pp. 452–456, 2014.
[8]
Y. Dover, J. Goldenberg, and D. Shapira, Network traces on penetration: Uncovering degree distribution from adoption data, Mark. Sci., vol. 31, no. 4, pp. 689–712, 2012.
[9]
E. M. Rogers, Diffusion of Innovations, 5th Edition, Simon and Schuster, 2003.
[10]
J. D. Bohlmann, R. J. Calantone, and M. Zhao, The effects of market network heterogeneity on innovation diffusion: An agent-based modeling approach, J. Prod. Innov. Manag., vol. 27, no. 5, pp. 741–760, 2010.
[11]
J. Goldenberg, B. Libai, and E. Muller, Talk of the network: A complex systems look at the underlying process of word-of-mouth, Mark. Lett., vol. 12, no. 3, pp. 211–223, 2001.
[12]
S. Pei, L. Muchnik, Jr. J. S. Andrade, Z. Zheng, and H. A. Makse, Searching for uperspreaders of information in real-world social media, Scientific reports, vol. 4, no. 1, p. 5547, 2014.
[13]
L. Liu, B. Qu, B. Chen, A. Hanjalic, and H. Wang, Modelling of information diffusion on social networks with applications to WeChat, Phys. A Stat. Mech. Appl., vol. 496, pp. 318–329, 2018.
[14]
O. Hinz, B. Skiera, C. Barrot, and J. U. Becker, Seeding strategies for viral marketing: An empirical comparison, J. Mark., vol. 75, no. 6, pp. 55–71, 2011.
[15]
D. J. Watts and P. S. Dodds, Influentials, networks, and public opinion formation, J. Consum. Res., vol. 34, no. 4, pp. 441–458, 2007.
[16]
C. Van den Bulte and S. H. K. Wuyts, Social Networks in Marketing. MSI Relevant Knowledge Series, 2007.
[17]
I. Singh and S. Singh, The hype machine: How social media disrupts our elections, our economy and our health- and how we must adapt, Bus. Soc. Rev., vol. 126, no. 1, pp. 101–104, 2021.
[18]
G. R. Krippner and A. S. Alvarez, Embeddedness and the intellectual projects of economic sociology, Annu. Rev. Sociol., vol. 33, pp. 219–240, 2007.
[19]
M. Nadini, L. Alessandretti, F. Di Giacinto, M. Martino, L. M. Aiello, and A. Baronchelli, Mapping the NFT revolution: Market trends, trade networks, and visual features, Sci. Rep., vol. 11, p. 20902, 2021.
[20]
M. Granovetter, Threshold models of collective behavior, Am. J. Sociol., vol. 83, no. 6, pp. 1420–1443, 1978.
[21]
E. Kiesling, M. Günther, C. Stummer, and L. M. Wakolbinger, Agent-based simulation of innovation diffusion: a review, Cent. Eur. J. Oper. Res., vol. 20, no. 2, pp. 183–230, 2012.
[22]
Ethereum improvement proposals, ERC-721: Non-fungible token standard, https://eips.ethereum.org/EIPS/eip-721, 2018.
[23]
Y. Chandra, Non-fungible token-enabled entrepreneurship: A conceptual framework, J. Bus. Ventur. Insights, vol. 18, p. e00323, 2022. [23] T. Sharma, Z. Zhou, Y. Huang, and Y. Wang, “It’s A blessing and A curse”: Unpacking creators’ practices with non-fungible tokens (NFTs) and their communities, arXiv preprint 2201.13233, 2022.
[24]
A. Guadamuz, The treachery of images: non-fungible tokens and copyright, J. Intellect. Prop. Law Pract., vol. 16, no. 12, pp. 1367–1385, 2021.
[25]
A. C. Moreaux and M. P. Mitrea, Royalty-friendly digital asset exchanges on blockchains, IEEE Access, vol. 11, pp. 56235–56247, 2023.
[26]
C. Münzel, P. Plötz, F. Sprei, and T. Gnann, How large is the effect of financial incentives on electric vehicle sales? –A global review and European analysis, Energy Econ., vol. 84, p. 104493, 2019.
[27]
G. Simpson and J. Clifton, Testing diffusion of innovations theory with data: Financial incentives, early adopters, and distributed solar energy in Australia, Energy Res. Soc. Sci., vol. 29, pp. 12–22, 2017.
[28]
M. Granovetter, Economic action and social structure: The problem of embeddedness, Am. J. Sociol., vol. 91, no. 3, pp. 481–510, 1985.
[29]
B. Uzzi, Embeddedness in the making of financial capital: How social relations and networks benefit firms seeking financing, Am. Sociol. Rev., vol. 64, no. 4, p. 481, 1999.
[30]
B. Uzzi and R. Lancaster, Embeddedness and price formation in the corporate law market, Am. Sociol. Rev., vol. 69, no. 3, pp. 319–344, 2004.
[31]
F. Polidoro Jr, G. Ahuja, and W. Mitchell, When the social structure overshadows competitive incentives: The effects of network embeddedness on joint venture dissolution, Acad. Manag. J., vol. 54, no. 1, pp. 203–223, 2011.
[32]
Z. Simsek, M. H. Lubatkin, and S. W. Floyd, Inter-firm networks and entrepreneurial behavior: A structural embeddedness perspective, J. Manag., vol. 29, no. 3, pp. 427–442, 2003.
[33]
D. Dequech, Cognitive and cultural embeddedness: Combining institutional economics and economic sociology, J. Econ. Issues, vol. 37, no. 2, pp. 461–470, 2003.
[34]
J. Beckert, Economic sociology and embeddedness: How shall we conceptualize economic action? J. Econ. Issues, vol. 37, no. 3, pp. 769–787, 2003.
[35]
N. Crossley, Towards Relational Sociology. Routledge, 2010.
[36]
J. A. W. Cruz, C. O. Quandt, H. T. Kato, R. da Rocha Rosa Martins, and T. S. Martins, How does the structure of social networks affect the performance of its actors? –A case study of recyclable materials collectors in the Brazilian context, Resour. Conserv. Recycl., vol. 78, pp. 36–46, 2013.
[37]
J. -H. Oh, Economic incentive, embeddedness, and social support: A study of Korean-owned nail salon workers’ rotating credit associations, Int. Migr. Rev., vol. 41, no. 3, pp. 623–655, 2007.
[38]
Y. Du, C. Gao, Y. Hu, S. Mahadevan, and Y. Deng, A new method of identifying influential nodes in complex networks based on TOPSIS, Phys. A Stat. Mech. Appl., vol. 399, pp. 57–69, 2014.
[39]
S. Jain and A. Sinha, Identification of influential users on twitter: A novel weighted correlated influence measure for covid-19, Chaos Solitons Fractals, vol. 139, p. 110037, 2020.
[40]
V. D. Blondel, J. -L. Guillaume, R. Lambiotte, and E. Lefebvre, Fast unfolding of communities in large networks, J. Stat. Mech., vol. 2008, no. 10, p. P10008, 2008.
[41]
K. B. Wilson, A. Karg, and H. Ghaderi, Prospecting non-fungible tokens in the digital economy: Stakeholders and ecosystem, risk and opportunity, Bus. Horiz., vol. 65, no. 5, pp. 657–670, 2022.
[42]
T. Zaucha and C. Agur, Newly minted: non-fungible tokens and the commodification of fandom, New Medium. Soc., vol. 26, no. 4, pp. 2234–2255, 2024.
[43]
D. M. Blei, A. Y. Ng, and M. I. Jordan, Latent dirichlet allocation, J. Machine Learning Res., 993–1022, 2003.
[44]
C. Van den Bulte and Y. V. Joshi, New product diffusion with influentials and imitators, Mark. Sci., vol. 26, no. 3, pp. 400–421, 2007.
[45]
J. Goldenberg, O. Lowengart, and D. Shapira, Zooming In: self-emergence of movements in new product growth, Mark. Sci., vol. 28, no. 2, pp. 274–292, 2009.
[46]
EMOJIALL, https://www.emojiall.com/en/all-emojis#categories-C, 2019.
[47]
D. Centola and M. Macy, Complex contagions and the weakness of long ties, Am. J. Sociol., vol. 113, no. 3, pp. 702–734, 2007.
[48]
L. McShane, E. Pancer, M. Poole, and Q. Deng, Emoji, playfulness, and brand engagement on twitter, J. Interact. Mark., vol. 53, no. 1, pp. 96–110, 2021.
Journal of Social Computing
Pages 385-402
Cite this article:
LI Z, ZHAO T, ZHU H. Gathering for Free: Embedding Economic Incentives in Social Networks Shape the Diffusion of NFTs. Journal of Social Computing, 2024, 5(4): 385-402. https://doi.org/10.23919/JSC.2024.0029
Metrics & Citations  
Article History
Copyright
Rights and Permissions
Return