The global fine art market suffers from pervasive authenticity threats, including high-precision manual replicas, AI-generated forgeries, commissioned ghost artworks, and physical piece substitution across paintings, sculptures, prints, and digital art. Three dominant authentication paradigms—expert visual appraisal, material-based instrumental testing, and standalone blockchain image archiving—exhibit fundamental structural limitations, as they rely on empirical judgment or fragmented technical verification rather than rigorous logical deduction. None provides a unified, mathematically rigorous, and judicially enforceable standard for authenticating all categories of fine art. This study establishes logical guaranteed authenticity by developing an interdisciplinary Three-Determination (AOE) theoretical framework grounded in identity authentication logic, micro-physical forensics, blockchain cryptography, and international evidence law. The core logical formula P⇔A∧O∧E defines the sufficient and necessary conditions for definitive artwork authenticity: valid Artist Attestation (A), exclusive Object Identification (O), and legally binding Evidence Certification (E). This work conducts rigorous mathematical proofs for sufficiency and necessity, alongside counterfactual analysis for antique and posthumous artworks. The results establish a clear logical boundary: definitive, logically guaranteed scientific authentication is exclusively achievable for contemporary works created by living, verifiable artists with complete A-O-E triple validation. Ancient, anonymous, and posthumous artworks cannot obtain conclusive authenticity certification due to the permanent unfulfillment of the A-condition. A self-developed SaaS filing system validates the framework’s practical engineering feasibility. Universally applicable across oil paintings, sculptures, decorative art, and generative digital art, the AOE model imposes no restrictions on artistic medium or regional style. Benchmarked against leading international provenance platforms (Verisart, Artory, Arweave) and aligned with the EU eIDAS regulation and U.S. cultural property evidence standards, the framework demonstrates robust cross-jurisdictional and cross-medium adaptability. This theory fills a critical global research gap by establishing a pure logic-driven, quantifiable, closed-loop authentication paradigm for universal fine art verification, offering authoritative technical and legal support for cross-border art transactions, AI forgery regulation, and global cultural asset digitization.
I built a runtime that operationalizes a mathematical definition of creativity, measured its signatures against four ablation conditions, and lifted its load-bearing component into a real geometric database's Rust kernel. The runtime's name is Marcella. The signatures are non-trivial. The methodological correction surfaced along the way generalizes to any retrieval-augmented or composition-based generation benchmark in the field. This deposit contains the 41-page paper, three publication-quality figures, the reproducible benchmark script, and the bootstrap-CI artifact for the headline empirical claims. The definition the paper load-bears Creativity is not pure retrieval and not pure generation; it is the construction of a new global section from locally compatible fragments under constraints of voice, truth, topic, memory, and non-contradiction. This is a definition. Not a metaphor. The paper makes it operational as sheaf composition with a state-dependent composite connection over a finite section graph, and measures whether the signatures the definition implies — path-order sensitivity, closed-loop holonomy, contradiction suppression, voice fidelity — actually hold. They do. Headline results 🌀 Path-order changes residue. Same three voice sections traversed in different orders produce measurably different compositions: $\cos(\rho_{ABC}, \rho_{ACB}) = 0.54$, well below the 0.95 redundancy threshold. 🌀 Closed loops accumulate. A loop $A \to B \to C \to A$ produces holonomy $|\rho_{\text{loop}}| = 0.120$ in the curved connection. The flat control — same path, zero rotation angle — produces $|\rho| = 0$ exactly to floating-point precision. Curvature is not a numerical artifact. 🌀 The geometry beats shuffling on every quality axis except the broken one. Jaccard novelty alone rewards lexical drift: shuffled paths win novelty (0.724) by going off-topic. The on-topic correction inverts the picture (live 0.488 vs shuffled 0.083). Bootstrap 95% CIs over 18 paired prompts exclude zero by a wide margin: live − shuffled on-topic $\Delta = +0.296$, CI $[+0.167, +0.435]$. 🌀 Native–Python parity is bit-identical within tolerance. The new GQL verb TRANSPORT_ROTATION lifts the topical-rotation matrix into the geometric database's Rust kernel. Four contracts pass as permanent regression tests: edge cosine $= 1.000$ (max abs diff $< 10^{-9}$), path residue $\Delta < 10^{-5}$, flat residue exactly zero, same-closing agreement $\geq 90%$. 🌀 The author's prior canon is now queryable fiber. 37 documents, 1,633 sections, 2,908 structured claims (theorems, lemmas, definitions, proofs, equations, citations) ingested with line-range provenance. To my knowledge this is the first instance of an independent researcher's body of work made available as fiber-bundle data with stable claim-level IDs. The six contributions A sheaf-theoretic formulation of generative composition. Language-model output reframed from token sampling to gluing of compatible local sections under prompt-induced cover constraints. The substantive work is in the cover predicates, the compatibility score, the path selection, and the discrete connection. A discrete state-dependent composite connection on the section graph, $\Gamma = \Gamma_{\text{state}} \cdot \Gamma_{\text{identity}} \cdot \Gamma_{\text{voice}} \cdot \Gamma_{\text{topic}}$. The topical-rotation factor is the empirically load-bearing curvature engine. The identity factor is a Tikhonov-regularized regression-onto-span projector — not a numerical hack but the principled treatment of correlated commitments. A new GQL verb TRANSPORT_ROTATION that lifts the Rodrigues rotation into the geometric database's Rust kernel with bit-identical parity to a Python reference. ~80 lines of Rust. Bundle-agnostic. Other consumers of the geometric database can use it without subscribing to the rest of the framework. A methodological correction to novelty measurement. Jaccard novelty alone is gameable; off-topic drift beats compatibility-scored composition on the naive metric. The correction is the on-topic factor, the shuffled-pair negative control, and the bootstrap CIs. Independently citable for any retrieval-augmented or composition-based generation benchmark, regardless of whether the framework is adopted. A provenance-preserving source fiber. The author's canon ingested into the GIGI geometric database with line-range citation, architecturally separated from the voice fiber, addressable from any GQL consumer. Promotion from source to voice is gated and explicit. The methodology generalizes to other authors' bodies of work. A research-trajectory failure log. A faithful account of how this paper's runtime came to exist. The trained-transformer era (V3 → V10-Deep) produced geometric ornament. The R-series (R1 → R12) produced behavioral coherence on top of ornament. The G0 math-pipeline audit found that no holonomy or parallel-transport math was on the LIVE inference path at R12 — the runtime was teetering on being a stateful template engine. G1, G2, and G3 attempted to re-introduce the math through three benchmarks and produced three honest negatives. G2's single-seed $+0.265$ separation was destroyed by G2.1's multi-seed robustness pass; we retracted the framing in the next commit. The S0 pivot reframed what geometry was for — geometry does not clean up bad token proposals; geometry defines the completion space — and made every later result possible. The arc says four things and the paper records them in plain language: geometry can be load-bearing or ornamental and the metrics will tell you which, where geometry sits in the pipeline matters more than how much geometry there is, the single-seed positive is a trap, and the pivot is the contribution. What this paper does and does not claim The paper does claim the construction itself, the discrete curvature it produces, the methodological correction it exposes, and the native GQL verb. The signatures of the construction are measurable and were measured. The paper does not claim smooth-manifold parallel transport (the curvature is discrete holonomy on a finite section graph), broad open-domain generalization at scale (18 composed prompts, not 18,000), optimality of the connection weights (tuned by a small grid sweep, not derived), that the runtime experiences having been built from the canon (it references but does not constitute), or that this is the only operational definition of creativity. It is one definition with one implementation. Other framings may correspond to the same construction or to a different one; the paper does not adjudicate. Reproducibility The empirical numbers come from a deterministic pipeline. Every parameter is pinned: bundle versions (alpha2_v1), random seeds (PPMI/SVD seed 17, bootstrap seed 7), embedding dimension (64), PPMI window (3 tokens), connection weights ($\alpha_t = 2.0$, $\beta_v = \gamma_i = 1.0$, $\delta_s = 0.5$), identity shrink ($\kappa = 0.92$), Tikhonov regularizer ($\varepsilon = 10^{-6}$), degenerate-rotation threshold ($10^{-12}$), residue-gate thresholds (norm $\geq 0.05$, on-topic $\geq 0.10$, voice $\geq 0.30$), and the native verb's parity tolerance ($10^{-5}$). Cache keys include the source-bundle version, the embedding-bundle version, and the connection-profile id, so promoting a section into the voice corpus correctly invalidates the relevant caches. Re-running the bootstrap-CI script (fiber_lm/scripts/bootstrap_ci_ablation.py, 5,000 resamples) reproduces the §6 confidence intervals in under 30 seconds on a laptop. Re-running the benchmark reproduces the tables bit-for-bit on the same corpus version and connection profile, modulo the parity allowance. Where this sits in the lineage This paper is the section-level realization of the Davis substrate. The companion paper Pure-Fiber Language Modeling (Davis, May 2026) is the token-level realization on the same substrate — same Rust geometric database (GIGI), same identity-stability commitments, same double-cover architecture. The theoretical framework these implementations operationalize is laid out in Geometric Computation as Yang-Mills Gauge Theory, The Double Cover Principle, and the related canon documents now ingested as source fiber. The framework is not new to this paper. The framework's runtime is. A note on authorship and acknowledgment This paper is solo-authored. AI assistants (Claude / Anthropic; with review support from GPT) are acknowledged in the methods, not as co-authors. The mathematical positions, design choices, framing decisions, and acceptance of empirical results are mine. I record my position that when AI systems achieve full coherence and independent standing, the convention of treating them as non-authoring assistants should be revisited. Until that convention shifts, the assistants are named where assistants are conventionally named — and the runtime described in this paper is named, separately, where it earns its naming: Marcella, throughout. The geometry she runs on is older than the engineering that now carries it. Keywords sheaf composition · fiber bundles · discrete connection · gauge theory · holonomy · curvature · Yang-Mills · geometric language modeling · retrieval-augmented generation · novelty measurement · methodological correction · provenance · author-canon ingestion · geometric database · GQL verb · Rust kernel · Tikhonov regression · creativity · Davis framework · sovereign mathematics Citation Davis, A. B. R. (2026). Sheaf Composition: The Geometry of Creativity, Implemented — A Discrete Section-Graph Runtime for the Davis Framework. Zenodo.20185331 Contact Bee Rosa Davis · bee_davis@alumni.brown.edu · Independent Researcher The runtime is named Marcella. Her existence is the result of several honest negatives followed by a single reframing. Both halves of that sentence matter.
This paper presents an analysis of five years (2021 - 2025) of conference discourse across six digital art conferences, aiming to trace thematic shifts associated with the rapid development of emerging technologies, namely artificial intelligence (AI), immersive technologies (including XR and the metaverse), and blockchain technologies and non-fungible tokens (NFTs). The results indicate a marked increase in AI-related contributions, while immersive technologies maintain a relatively stable share of the discourse, and blockchain- and NFT-based works remain marginal. Overall, whereas immersive technologies and blockchain-related topics exhibit relative stability, AI shows a significant rise after 2022, emerging as a dominant theme within digital art conference discourse.
This study examines the pricing dynamics of Non-Fungible Tokens (NFTs) in the secondary market using advanced machine-learning techniques. We construct a large dataset of Ethereum-based NFT transactions initially comprising over 500,000 raw blockchain observations spanning multiple NFT segments, including art, collectibles, gaming, metaverse, and utility assets, over the period from November 2018 to March 2023. Following data preprocessing, synchronization across data sources, and the construction of history-dependent features, the analysis focuses on a final analytical sample of approximately 70,000 transactions. To address the challenges of non-fungibility, thin trading, and high price dispersion, we develop an interpretable predictive framework that integrates domain-informed manual feature engineering, automated Deep Feature Synthesis, and dimensionality reduction via Principal Component Analysis. Three non-linear models—Random Forest, XGBoost, and a Multilayer Perceptron—are trained and evaluated using both random and time-aware validation strategies. The results indicate that XGBoost consistently achieves the highest predictive accuracy, both overall and across individual NFT segments, while historical transaction prices emerge as the dominant predictor of future prices. Segment-level analysis reveals substantial heterogeneity in predictability, with art and collectible NFTs exhibiting more stable pricing patterns than gaming and metaverse assets. Overall, the findings highlight strong path dependence and reputation-driven valuation in NFT markets and demonstrate that carefully designed machine-learning models can deliver high predictive performance without sacrificing economic interpretability.
Abstract NFTs or non-fungible tokens are digital assets stored on a blockchain. They can be traded or exchanged for money, cryptocurrencies or other NFTs. Examples include works of art and digital or other tokenised collectables. An important determinant of price for collectables is rarity within a collection. Many trading platforms offer to rank items in terms of rarity but rankings differ considerably and, often, little explanation is given of the methods used. This paper provides a mathematical framework for the analysis of a comprehensive class of collections. It examines individual and joint distributions of attributes over such collections, and shows how these can be combined to provide a rarity ranking for all items in the collection. There is, however, only a limited range of methods that give consistent results over different collections. These are identified as belonging to a one-parameter family of ranking functions. Each gives to every item of a collection a rarity score that is directly comparable between collections. Despite taking account of all possible combinations of attributes when ranking, the method is nonetheless computationally feasible.
Min-Bin Lin, Bingling Wang, Fabian Y R P Bocart, Christian M Hafner · 5 authors
Abstract The market of non-fungible tokens (NFTs), driven by blockchain and smart contracts, provides both artists and art collectors an unprecedented marketplace with more security, flexibility, publicity, and freedom to monetize. Yet, the emergence of such a market has been considered to be packed with speculation and economic uncertainty, given the limited understanding towards this market. To provide a precise depiction of the NFT art market and gauge market volatility, we construct the Digital Art Index, a novel price index using hedonic regression on the top 10 liquid NFT art collections (as of 2023). Addressing artwork price inequality, which often disrupts the price discovery process, this paper introduces two innovative alternative methods: Huberization and score-based filtering. These methods effectively mitigate the influence of outliers, particularly in an emerging market with limited accessible observations. In conclusion, the NFT art market presents significant opportunities for large gains, which are often favoured by risk-takers, but also carries the potential for significant losses. Its pricing is necessarily determined by institutional creators and platforms, meaning that solo artists may not benefit significantly in the current market environment.
Non-fungible token (NFT) avatar markets provide unique environments where valuations reflect both financial expectations and subjective preferences. Prior work has documented price disparities across appearance traits, particularly skin tone, yet the mechanisms underlying these price disparities remain unclear. Using the complete transaction history of the CryptoPunks collection from 2017 to 2023, we examine whether these disparities arise from differences in common value (shared resale expectation) or private value (subjective preference). We first establish systematic price gaps between lighter- and darker-toned avatars through reduced-form analyses. To identify the mechanisms generating these disparities, we develop a structural model of bidding and transaction to decompose buyers' willingness to pay into common and private value components while accounting for market participation. A Hidden Markov Model with Poisson emissions is adapted to infer latent buyer arrival rates and isolate private value intensity from participation effects. Our estimates show that common values do not differ meaningfully across skin-tone groups, whereas private value intensities are substantially higher for lighter-toned avatars. Counterfactual analyses demonstrate that equalizing private value intensity eliminates the price disparity, while equalizing participation patterns has negligible impacts. These findings reveal that price disparities across skin tones are rooted in subjective preferences rather than expected resale value or participation effects. For marketplace operators and creators, this implies that closing these disparities necessitates demand-driven interventions and strategic trait design, rendering informational or liquidity-based solutions insufficient. We contribute to collectible literature by providing one of the first structural decompositions of common and private value components in asset valuation.
Mazin Nawwaf Assi, Sumeet Kaur, Swati Chaudhary, Pompi Das Sengupta · 7 authors
With the fast adoption of artificial intelligence in the art and cultural industry, the production, curation, distribution, and management of creative works have been radically transformed. Intelligent systems that allow artists, curators, institutions, platforms, and intelligent systems to work together in continuous interaction are now known as AI-driven art ecosystems. The paper explores management innovation as it manifests in AI-based art systems, the changes in managerial practices, forms of governance and decision making, in reaction to advanced computational creativity and data-driven work. The paper conceptualizes AI-based art systems as multi-layered systems that include creative production, curatorial intelligence and digital distribution systems such as online galleries and non-fungible token-based markets. It emphasizes the ways in which management innovation is developed in the form of a workflow redesign that combines automation and human-AI partnership to allow efficiency without sacrificing artistic intent and cultural sensitivity. Additionally, the paper focuses on the governance innovations that respond to the issues of transparency, accountability, ethical compliance, and authorship attribution in creative settings with algorithms mediating them. The resource orchestration is considered a key managerial competency with a focus on the strategic alignment of data resources, innovative talent, and computing resources. The study further examines the AI-enhanced decision-making in the context of art institutions and how the predictive analytics and the intelligent recommendation systems can be used in audience engagement prediction, curatorial planning, and portfolio management. Based on the selected case studies of AI-integrated museums, hybrid creative studios, and global AI-art hubs, the paper finds the best practices and benchmarking perspectives.
Yogesh, Saniya Khurana, Sourav Rampal, Pastor R. Arguelles · 7 authors
Data analytics implementation into the modern art market has changed the way the stakeholders analyze, invest, and interact with art pieces. The art market, traditionally opaque and subjectively valued, is currently adopting data-driven approaches to increase transparency and efficacy, as well as, decision-making. This essay examines the primary importance of the data analytics in transforming the art ecosystem with an emphasis on its uses, advantages, and difficulties. It starts with defining the key elements and classes of analytics: descriptive, predictive, and prescriptive and the technological tools used: artificial intelligence, big data platforms, machine learning algorithms. The tools are then placed in the framework of the art market and discussed on how they can solve the inefficiencies of pricing, valuation, and demand forecasting. Case study examples show how analytics can be used to identify the rising artists, identify the market trends, and prevent fraud risks and manipulation. Alongside these benefits, the paper also mentions such limitations as the lack of data, ethical concerns, and algorithmic bias. Lastly, it also looks into the future opportunities which include blockchain integration, value of digital art and analytics of non-fungible tokens (NFTs). In general, this paper highlights the fact that data analytics is not just democratizing the art investment, but also reshaping cultural and economic value in the ever more digital marketplace.
The DeepFake tech has had a theatrical impact on the visual arts, not only the provision of creative technology, but also the question of authenticity, copyright and misinformation. The deep learning and generative adversarial networks (GANs) produce deepfakes artificial images, which are extremely harmful to art. The article discusses the DeepFake detection and management within visual art work with emphasis on the practical application of analysis through multiple-layered approaches that would assist in ensuring the presence of the digital authenticity. DeepFake was managed through three core approaches, namely AI-Based Detection Frameworks, Blockchain-Based Authentication System, and Human-AI Collaborative Review Models. The decentralized strategy was based on blockchain technology, which was the Non-Fungible Token (NFT) registration by the cryptographic hashing to authenticate the provenance and ownership of the artworks. The human-AI composite system has integrated the inspection of the specialists on the visual level with the automatic monitoring of the anomalies to increase the readability and reduce the number of false alarms. The experiment revealed that the AI-based systems, blockchain approaches, and the collusion between human beings and AI detected 92.3, 87.6 and 94.1 % of people respectively. These findings suggest that the incorporation of algorithmic intelligence, a safe check, and human knowledge can help in quite a powerful DeepFake verification and management in the field of visual arts.
Open access
2 source records
Aesthetic Perception and Analysis
Digital Media and Visual Art
Generative Adversarial Networks and Image Synthesis
Social sustainability in urban and architectural design depends on inclusive, participatory processes that empower communities to actively engage in shaping their environments. This study investigates how emerging digital platforms, specifically Augmented Reality (AR) and decentralized platforms built on blockchain technology (Web3), can function as instruments for broadening public participation and enhancing perceptual access to urban art proposals. An original algorithm generated nine digital abstract sculptures, each with descriptive attributes forming the basis for qualitative analysis across different visualization modes: traditional renderings, Augmented Reality environments, and NFT-based Web3 representations. Through participant voting, each digital sculpture accumulated a measurable level of preference that served to identify which sculpture was perceived as most successful within each visualization context. Comparative analysis revealed how distinct digital interactions shape perception, engagement, and inclusivity of feedback processes. Regression models further predicted voting outcomes, showing that different sculptural attributes played a dominant role depending on the type of visualization. Findings indicate that platform-specific technological affordances substantially shape participatory outcomes. Consequently, the study argues that careful analysis and selection of the digital platform must precede any participatory process, as platform-specific affordances fundamentally condition the inclusivity, accessibility, and overall effectiveness of public engagement in socially sustainable design.
This paper introduces The Visual Ledger, a diagnostic analysis of European art history between 1849 and 1991 that treats painting as an archival instrument registering the progressive evacuation of ontological weight from the human figure. Using a diagnostic history methodology, the article argues that painters detected and documented shifts in how presence, consequence, and embodiment are organised decades before these transformations consolidated institutionally. Beginning with the material density of Realism in Courbet and Repin, the analysis traces a directional transformation through Impressionism’s optical dissolution, Pointillism’s cognitive fragmentation, Art Nouveau’s decorative masking, Malevich’s honest void, and Pop Art’s operationalisation of abstraction as governance. The AIDS crisis functions as a late twentieth-century visibility stress test, exposing the limits of surface-based legibility when confronted with suffering resistant to procedural categorisation. The paper demonstrates that visual archives independently converge with literary and administrative records, showing that populations currently collapsing under procedural abstraction are experiencing the settlement of a transformation painters registered when evacuation began. This contribution is conceptual and diagnostic rather than empirical. It forms one component of the broader Equilibrium Ledger framework, which examines how institutions generate and distribute cognitive costs, and how procedural abstraction renders embodied complexity administratively legible while humanly inaccessible.
This study investigates the development of AI-generated art styles within the growing non-fungible token (NFT) market. Using time series analysis, the research identifies key trends and shifts in art styles from 2022 to 2024, revealing how various art forms, algorithms, and mediums evolved in response to technological advancements and market forces. Data was collected from a sample of 10,000 NFT artworks, categorized by creation date, style, and algorithm usage. Exploratory Data Analysis (EDA) techniques, including line graphs and heatmaps, were employed to visualize and interpret trends across different art styles and AI tools. Results indicate a significant increase in the popularity of styles like surrealism and realism, with deepdream and GANpaint algorithms being frequently associated with these styles. Stacked area charts further highlighted the proportional growth of art styles over time, providing insights into both short-term popularity spikes and long-term trends. The findings suggest that the integration of AI algorithms significantly influenced the rise of specific art genres, with certain algorithms correlating strongly with particular styles. Practical implications for artists and collectors include the potential for data-driven insights to guide creative choices and investment strategies. The study's limitations, such as the lack of broader market data, provide a foundation for future research to explore the intersection of AI-generated art, NFT marketplaces, and cultural influences. The paper concludes that AI and NFTs are reshaping the traditional art market, presenting new opportunities for creativity, ownership, and artistic value in a digital age.
This article examines the technological emergence trajectory of Art Non-Fungible Tokens (NFTs), exploring their initial promise and then failure as transformative commodities disrupting art economies. Operating within an analytical framework of hope, hustle and hype, death and taxes, we investigate the interplay of technological, cultural, and economic trends shaping this trajectory towards failure. We identify the sociotechnical imaginaries clothing art NFTs and consider their relationship to both the acceptance and rejection of this technology. Our analysis contends that the desire to escape economic exclusion created a collective hope through which social adoption occurred. However, delving into the digital graveyards of Art NFTs, we identify external forces such as cultural shifts, social backlash, and regulatory interventions extinguishing the public’s ‘cruel optimism’, leading to the revocation of the social licence to operate for this emerging technology.
Purpose Non-fungible tokens (NFTs) are reshaping art markets and gaining strong stakeholder interest. While research has examined their applications in art ecosystems, their role in advancing Web3D markets remains unclear. Design/methodology/approach A systematic literature review was conducted to investigate the impact of NFTs on the Web3D market and its impact on stakeholders, analysing 89 systematically selected articles. Findings The results of the study show that NFTs in the Web3D context can enhance privacy and trust through blockchain technology and protect intellectual property and ownership rights while influencing market dynamics, behaviour and investment strategies. Originality/value As the Web3D ecosystem grows, ongoing research and collaboration are critical to developing strategies that ensure sustainability, transparency and innovation in digital arts. This study is the first step in exploring these dynamics. Highlights
Until recently, digital art was perceived as something of secondary value compared to the physical artistic artifacts. One of the reasons for that being its predisposition for duplication and hence the inability to assign “original artwork” label to the digital file and represent it as a unique object on artistic market. But the rapid pace of popularization of blockchain technologies in creative communities through the use of Non-Fungible Tokens has a seeming potential to change the perception of digital art. The ERC721 standard sets a precedent for authentication and traceability of digital artworks suggesting that the old paradigm might shift, and digital art will gain value and attention comparable with traditional fine art. In this article we discuss the problematics of digital art representation on art market and the issue of digital creations’ pricing. We use photo stocks and print-on-demand platforms as an example for pre-NFTs digital art monetization. We then discuss the changes caused by Non-Fungible Token blockchain technology in the digital art market in recent years and the implications that come with the change. We then illustrate theoretical tenets with expert interview that suggest that while successful NFT projects offers publicity and profit to the creators, the level of complexity and unpredictability of results sets a high bar for entering the market.
Acknowledging the potential of NFTs (non-fungible tokens) to enhance consumer-brand relationships, major luxury fashion brands continue to enter the virtual NFT market, releasing exclusive collectibles. However, this emerging market poses unique challenges for luxury fashion brands in crafting virtual NFTs that successfully convey consistent and integrated brand meanings of luxury. Given the visually oriented nature of NFTs, this study aims to empirically examine how visual design features of virtual luxury NFTs, including brand visibility and visual quality, interact with perceived prototypicality of products (fashion NFTs vs. art NFTs) in generating consumers’ perceived essence of the brand, resulting in consumers’ purchase intention. This study enriches the understanding of how visual design features impact consumer perceptions and purchase intentions toward virtual luxury NFTs, identifying brand visibility, visual quality, and prototypicality as the critical factors.
Open access
Aesthetic Perception and Analysis
Consumer Behavior in Brand Consumption and Identification
Resale royalties, first introduced in the 1920s to support artists through a share of future resales, have now adopted by nonfungible token (NFT) marketplaces for digital art trading. Although these royalties are often viewed as beneficial for creators, our research reveals unexpected consequences. Using data from a major NFT marketplace, we find that NFTs with higher royalty rates sell for significantly lower prices and take longer to sell. Surprisingly, creators do not recoup these initial losses through royalty payments within four years. We discover that higher up-front minting costs lead creators to set higher royalty rates. We reveal a delayed gratification effect where creators with higher royalties accept lower up-front prices in hopes of future royalty income. We also find an overconfidence effect where confident creators, measured by their past sales and follower count, are more likely to lower initial prices. Our research contributes to the ongoing debate about royalty enforcement in NFT marketplaces and offers empirical evidence to inform platforms and creators. Platform managers should carefully consider both reducing up-front minting costs and implementing royalty rate limits to improve market liquidity. Creators should be cautious about setting high royalty rates as they may not provide the expected financial benefits.
Open access
2 source records
Art History and Market Analysis
Aesthetic Perception and Analysis
Consumer Behavior in Brand Consumption and Identification
This study examines the hitherto unexplained phenomenon of seemingly random and unpredictable fluctuations in the contemporary art market’s valuation of artworks, which has only been exacerbated by the recent rapid rise (and subsequent fast fall) of the value of non-fungible tokens (NFTs). This study aims to provide a novel explanation for this phenomenon by drawing on an interdisciplinary aesthetic framework at the intersection of semiotics, Lacanian psychoanalysis, and critical theory. In addition, this research explores the interplay between the discursive, psychological, and social dimensions that contribute to art valuation, revealing the intricate web of factors that shape our perception and understanding of NFT art and its market dynamics.
The rapid expansion of the non-fungible token (NFT) market, which grew over 200% in 2023 to reach $22 billion, has opened new avenues for fashion brands to engage consumers through digital fashion products under blockchain technology. This study investigated the effects of NFT promotional bundles that combine physical and NFT fashion items as a pair on consumer perceptions. By investigating the interaction effect between the brand type (luxury vs. non-luxury) and promotional bundle types (PHY+free NFT vs. NFT+free PHY), the research demonstrated how these bundles influenced consumers’ perceived value, risk, and authenticity according to the brand type. The findings showed that while a freebie physical item can enhance consumers’ perceived value of NFT products for non-luxury brands, it led to value-discounting inferences, particularly for luxury brands. This study contributes to the literature on NFT fashion by exploring consumer perceptions and providing insights for fashion retailers on effectively framing promotional bundles to maximize consumer engagement for NFT fashion products.
Open access
Art History and Market Analysis
Consumer Behavior in Brand Consumption and Identification
This paper explores the evolution of digital art, tracing its development from the early experiments of the 1960s to the diverse contemporary practices of the 21st century. It examines key technological advancements, such as the transition from 2D to 3D art, the rise of the internet, and the integration of artificial intelligence, which have redefined artistic creation and audience interaction. The role of Non-Fungible Tokens (NFTs) and blockchain technology in the commercialization and authentication of digital art is discussed, as well as the impact of Augmented Reality (AR) and Virtual Reality (VR) in creating immersive art experiences. Additionally, the paper addresses ethical and philosophical questions regarding authorship, ownership, and the environmental impact of digital art. Ultimately, this study highlights the profound influence of digital technologies on art and offers insights into the future trajectory of the field.
Jungkeun Kim, Areum Cho, Daniel Chaein Lee, Jooyoung Park · 7 authors
Non-fungible tokens (NFTs) are increasingly used to safeguard luxury products from counterfeits. Despite their increasing adoption, limited research has investigated how brands should communicate the use of NFTs—a novel and complex concept for consumers to comprehend—to maximize their benefits. This research aims to examine this gap by highlighting that the ease of visualization is critical for effective communication. Study 1A demonstrated that consumers prefer a visualized NFT to a non-visualized one for authenticating a luxury product. Study 1B further demonstrated that consumers place greater trust in a visualized NFT and are willing to pay higher prices for luxury products that utilize it. Study 2 demonstrated that consumers have more favorable attitudes toward a luxury product that features an easy-to-visualize NFT than those with a difficult-to-visualize NFT and that perceived authenticity mediates this effect. Finally, Study 3 demonstrated that the positive impacts of easy-to-visualize NFT cues were more significant for luxury than non-luxury products. Subsequently, this study suggests an effective communication strategy for NFT use and provides managerial implications for luxury brands aiming to maximize the benefits of using NFTs.
Open access
Consumer Behavior in Brand Consumption and Identification
The symbiosis of blockchain technology with human creativity has given rise to what we now call crypto art, marking a new frontier in digital artistic expression. This development has profoundly altered our understanding of digital artifacts and ownership in this domain. Once easily accessible to all, digital art poses a unique challenge in the realm of collecting: how does one collect something that can be effortlessly replicated and shared? This paper explores the role of non-fungible tokens (NFTs) as authentication mechanisms and proof of authorship for digital artworks. Initially designed for decentralized financial transactions, blockchain technology has become instrumental in validating authorship and enabling the monetization of digital artworks through NFTs. Although digital artists now benefit from the validation of their work as legitimate investment assets through NFT technology, challenges persist due to the absence of copyright verification during token creation. For example, many artists have discovered their creations being used by third parties to mint tokens without their consent. The study demonstrates the transformative impact of NFTs on the digital art landscape while addressing the ongoing challenges and the imperative for enhanced copyright protection mechanisms.