Businesses lose millions of dollars every year when they canât restore data from backups. Research shows that Disaster Recovery Plan (DRP) testing is not conducted frequently enough, nor are records maintained that demonstrate full data recovery from backups. This work introduces a design science artifact called PRTOK that aims to increase DRP testing. The design science artifact is a software solution that integrates with Data Management Systems (DMS) such as iRODS and DSpace, and can work with formats such as HDF5 and BagIt. Proof-of- recovery records, or tokens, are recorded in a replicated, resilient, and indelible proof-of- authority blockchain data structure. Access to the PRTOK blockchain allows practitioners restoring data from any backup software program or DMS integrated with PRTOK to view metadata about restoration operations. Metadata in the PRTOK record includes validation checksums of fully restored data, timestamps, and a documented record of how the data was restored, so that restoration operations can be repeated using those instructions. PRTOK is evaluated for validity within design science frameworks and theories, for both utility and socio- technical contributions to the state of the art in DRP testing. Qualitative data is collected from an online survey that includes a video and an interactive demonstration of the design artifact. Docusign Envelope ID: E4BB68B4-E66F-49EF-8B9D-F60A4B698376 x Participants in the survey are asked questions about their experience with DRP testing and whether and how the introduction of PRTOK contributes to the utility and ease of use of DRP testing processes. The evaluation of the PRTOK artifact included both feature demonstration and validation of knowledge claims against the Larsen framework for design science evaluation (Larsen et al., 2025). A socio-technical survey was also conducted to gain qualitative insights into whether and how practitioners found the PRTOK instantiation useful, whether it improved DRP testing, in what ways it was disadvantageous, and in what areas it needed improvement. The first research question (RQ1) asks what the characteristics of a data integrity model that improves data value by providing evidence of data recovery are. The feature set of the PRTOK instantiation, along with its model description, shows that these characteristics were achieved in the design and implementation of the PRTOK model. The socio-technical survey results show that the majority of respondents found the PRTOK implementation useful for DRP testing, although they also identified areas for improvement and some disadvantages of PRTOK. The second research question (RQ2) asks what risks to VDA are introduced by such a model and how those risks can be mitigated.
Odinachi Udemezuo Nwankwo, Simeon Okechukwu Ajakwe, Muhammad Rasyid Redha Ansori, Gifar Arif Haryadi ¡ 6 authors
Existing driver distraction detection systems face critical barriers to real-world deployment in safety-critical transportation environments, including the lack of real-time edge inference, explainable artificial intelligence (XAI), trustworthy event logging, and privacy-preserving evidence management. To overcome these challenges, this paper presents an integrated framework, termed DRIVERDAPP , that unifies real-time edge-based detection, AI explainability, and secure, auditable event management. Redâgreenâblue (RGB) in-cabin image frames captured by a dashboard camera are processed locally on an NVIDIA Jetson Nano edge device, where a fine-tuned You Only Look Once version 11 small (YOLOv11s) model classifies ten driver behavior states and triggers in-vehicle audio alerts for unsafe activities. To suppress transient misclassifications under edge constraints, distraction persistence is verified using a lightweight temporal confirmation strategy. Confirmed distraction events are immutably recorded via Solidity-based smart contracts and submitted through the Web3.py interface to a permissioned Hyperledger Besu consortium blockchain operating under Quorum Byzantine Fault Tolerance (QBFT) consensus. Privacy is preserved by retaining raw visual data off-chain, while only pseudo-anonymous identifiers and event metadata are stored on-chain under controlled access policies. Model interpretability is enabled using Gradient-weighted Class Activation Mapping (Grad-CAM), providing transparent visual explanations of distraction-related predictions. The framework is evaluated using the State Farm Distracted Driver and American University in Cairo datasets, demonstrating stable real-time edge operation, negligible blockchain query latency, and secure smart contract execution. These results confirm the suitability of DRIVERDAPP for secure, explainable, and deployable driver monitoring in intelligent transportation systems.
The circuit-breaker capstone of the WANDERING arc on long-horizon coding-agent failure. A prior result ('The Lever Is Late') showed that control of a coding agent's 'finish' decision lives not at the mid-layer 'task-is-done' verdict but in a late, task-matched action-commitment block ~30 layers downstream. This paper answers two pre-registered questions that the single 'finish' result could not: is the late lever SPECIFIC to termination, and can it BRAKE an action, not just elicit one? On Qwen3.6-27B over 99 SWE-bench Pro trajectories, using a second decision in the same data -- commit a file edit (str_replace_editor) vs. continue reversible exploration (bash) -- with n=60 deterministic decision points per condition, prefill-only patching, and generation-confirmed outcomes: (1) GENERALIZATION (elicit): injecting a task-matched edit-donor into the late block makes a stuck-in-exploration agent emit a real edit call (0.23 -> 0.77 at L59; position control 0.08, cross-task control 0.48). (2) THE BRAKE (suppress): injecting an explore-donor at a commit decision collapses the real edit rate 0.48 -> 0.02 (96% suppression) at L55, with a same-class control intact (0.55) and the opposite donor boosting to 0.92. (3) The mechanism is MONOTONIC and BIDIRECTIONAL: exact paired McNemar on all 14 per-point conditions yields seven contrasts surviving Holm-Bonferroni (worst p=7.6e-5), with elicit c=0 (the edit-donor only turns commits on) and brake b=0 (the explore-donor only turns them off) -- the lever moves exactly in the donor's direction with ~zero off-direction noise. (4) CROSS-ARCHITECTURE: the late-commitment geometry and donor-specific writability replicate across two model families and two scales (Mistral-7B and the scale-matched Mistral-Small-24B, where the mid-inert / late-write dissociation is cleanest: fidelity 0.955 vs 0.007). Strengtheners: the elicit/brake lift survives a full valid-tool-call re-parse (0.23->0.37 elicit, 0.40->0.07 brake), and the brake re-routes to reversible exploration (+0.17 bash above its no-brake floor of 0.43). We frame the bidirectional late lever as the mechanism for a mechanistic CIRCUIT-BREAKER: a single late-layer intervention that blocks an action at its commit point. Honest scope: demonstrated on a state-mutating but UNDOABLE edit (a semi-irreversible proxy); intervening on a genuinely irreversible action (e.g. send_transaction) is the named next step. The model-agnostic decision-locator tool, pre-registrations, per-point data, exact-statistics script, and an adversarial pre-publication evaluation are released in the GitHub repository under paper/circuit_breaker/. EXTENDED EDITION adds a mechanistic decomposition of the lever (Section 'Opening the lever: a sparse attention-head circuit'). Using an exact additive residual split (y=x+attn+mlp; reconstruction relerr 0.0025) the elicit is written by the L59 ATTENTION sublayer (MLP null; Wilcoxon attn>>mlp p=1.8e-8; direction-specific 2.2x), while the brake localizes to NO sublayer (distributed, super-additive residual) -- the elicit/brake asymmetry holds at sublayer resolution and rules out a feed-forward key-value write. One level deeper, the elicit is a SPARSE 3-head push circuit at L59 (heads 8/6/3 reproduce and overshoot the full attention effect, top-3 +0.262 >= all-24 +0.224; emit 0.23->0.42), partially opposed by a counter-set; geometric write-magnitude misleads (the largest writer is causally an opponent). These heads attend globally to the trajectory's TOOL-CALL HISTORY (an induction/copy signature), not a semantic verdict. A source-content knockout gives partial/directional causal support (tool choice is causally specific to each tool's name tokens: ablating 'bash' tokens drops P(bash) -0.071 vs ~0 for random; the edit side is ceiling-confounded). All 53 reported numbers were verified against the released per-result ledgers by an adversarial pre-submission evaluation (EVAL_mechanism.md). Scripts (commit_lever_decomp/heads/attn/knockout.py) and per-result ledgers are released.
Surya Bahadur Kathayat, Magnus Svendsen, Brage Hagemann Brataas
Web3 applications strive to enable decentralization and user sovereignty, but often remain inaccessible to mainstream users due to complex onboarding and unfamiliar interaction paradigms. This study presents a Web2-inspired onboarding solution that integrates an embedded custodial wallet with OpenID Connect (OIDC) authentication via Vipps, a Norwegian bank-backed identity provider with over 4.6 million verified users. The proposed approach abstracts wallet management and removes the need for seed-phrase setup while introducing real-world identity assurance into the Web3 environment. A blockchain-based Battleship proof-of-concept was developed to demonstrate the approach, aiming to make Web3 interactions more intuitive and trustworthy. A mixed-method evaluation, combining usability testing and semi-structured interviews, revealed that integrating familiar login flows with verified identities improves usability, conceptual understanding, and both peer and ecosystem trust. The findings suggest that leveraging centralized identity providers can act as a pragmatic bridge between Web2 and Web3, potentially lowering initial onboarding barriers.
Money laundering enables malicious actors to integrate illegal profits into the legitimate economy and has long been a central concern in financial regulation. Blockchain systems introduce new channels for laundering through decentralized, pseudonymous, and cross-border asset transfers. In this context, blockchain exploiters often rely on laundering to conceal fund origins and enable cash-out.
Process attestation verifies human authorship by collecting behavioral biometric evidence, including keystroke dynamics, typing patterns, and editing behavior, during the creative process. However, the very data needed to prove authenticity can reveal intimate details about an author's cognitive state, health conditions, and identity, constituting sensitive biometric data under GDPR Article 9. We resolve this privacy-attestation paradox using zero-knowledge proofs. We present ZK-PoP, a construction that allows a verifier to confirm that (a) sequential work function chains were computed correctly, (b) behavioral feature vectors fall within human population distributions, and (c) content evolution is consistent with incremental human editing, all without learning the underlying behavioral data, exact timing, or intermediate content. Our construction uses Groth16 proofs over arithmetic circuits with Pedersen commitments and Bulletproof range proofs. We prove that ZK-PoP is computationally zero-knowledge, computationally sound, and achieves unlinkability across sessions. Evaluation shows proof generation in under 30 seconds for a 1-hour writing session, with 192-byte proofs verifiable in 8.2 ms, while incurring less than 5% accuracy loss in simulation at practical privacy levels (epsilon >= 1.0) compared to non-private baselines.
Oshani Seneviratne, Fernando Spadea, Adrien Pavao, Aaron Micah Green ¡ 5 authors
Temporal Web analytics increasingly relies on large-scale, longitudinal data to understand how users, content, and systems evolve over time. A rapidly growing frontier is the \emph{Temporal Web3}: decentralized platforms whose behavior is recorded as immutable, time-stamped event streams. Despite the richness of this data, the field lacks shared, reproducible benchmarks that capture real-world temporal dynamics, specifically censoring and non-stationarity, across extended horizons. This absence slows methodological progress and limits the transfer of techniques between Web3 and broader Web domains. In this paper, we present the \textit{FinSurvival Challenge 2025} as a case study in benchmarking \emph{temporal Web3 intelligence}. Using 21.8 million transaction records from the Aave v3 protocol, the challenge operationalized 16 survival prediction tasks to model user behavior transitions.We detail the benchmark design and the winning solutions, highlighting how domain-aware temporal feature construction significantly outperformed generic modeling approaches. Furthermore, we distill lessons for next-generation temporal benchmarks, arguing that Web3 systems provide a high-fidelity sandbox for studying temporal challenges, such as churn, risk, and evolution that are fundamental to the wider Web.
Trust (or distrust) in artificial intelligence (AI) is a critical research topic, given AI's pervasive integration across societal domains. Despite its significance, scholarly attention to process-based learned trust in AI remains limited. To address this gap, this study designed a virtual non-fungible token (NFT) investment task, featuring seven rounds of risk decision-making scenarios, to simulate an investment/trust game to explore participants' multifaceted trust under the influence of different chatbots' social role. The findings suggested the chatbot's social role had a significant impact on participants' trust behaviors and perceptions over time. Trust in the two chatbot types diverged until the system-induced failures occurred. The friend-like chatbot elicited a higher level of behavioral trust than the servant-like counterpart. During those trust-damaging moments, the friend-like chatbot proved more effective in mitigating trust erosion and facilitating trust repair, as evidenced by relatively stable investment behaviors. The findings reinforce the notion that friendship with AI can function as a relational buffer, softening the impact of trust violations and facilitating smoother trust recovery.
Blockchain technology is a modern day decentralized databases which presents unique usability changes like âtransaction irreversibility, decentralized trust models, and cryptographic key managementâ which places excessive responsibilities on users that can lead to financial loss and hinders its adoption. Failing to address these usability changes, the current heuristic evaluation models like Nielsen's ten principles need to be revised. Therefore, this paper aims to investigate whether new heuristics are needed to evaluate blockchain applications effectively. During the investigation a critical gap was identified in establishing usability evaluation methods, demonstrating that Nielsen's widely adopted heuristics failed to address 40% of common blockchain-specific usability problems (e.g., users sending funds to incorrect addresses due to poor reversibility awareness). 33 empirical studies were analyzed using systematic review and metaanalysis, mainly focused on blockchain wallets, DeFi (Decentralized Finance) platforms, NFT (Non-Fungible Tokens) marketplaces and Decentralized Autonomous Organizations (DAOs). A set of core usability issues were extracted and thematic analysis was conducted to develop four new blockchain-specific heuristic principles:(1) Transaction Finality Awareness (explicit warnings for irreversible actions), (2) Cryptographic Self-Custody Affordance (intuitive key/seed phrase management), (3) Fee Transparency & Control (realtime gas fee estimation), and (4) Decentralized Value Proposition Clarity. This paper proposes a validation methodology via heuristic evaluation of three blockchain applications by five experts would show a significant increase in detected usability flaws when using the extended heuristics versus Nielsen's original set, projected at approximately 31% based on our gap analysis. Reducing the risk of user errors will require a tailored criteria that will provide experts domain specific heuristics to create a user-friendly Dapp. Thus, this paper not only aims to create a novel domain specific heuristic evaluation for blockchain experts to use as a pre- deployment evaluation tool but also to create safer UX (user experience) for dApps.
Thanassis Tiropanis, George Roussos, Mohammad Bahrani, Mohamed Ragab
The growing demand for data ownership and privacy is reshaping how information is accessed, managed, integrated, and recommended. Building on the inaugural DESERE workshop at The Web Conference 2024, this second edition advances research on Decentralised Search and Recommendation platforms such as Personal Online Datastores (PODs), where users retain control of their data and explicitly manage permissions. As ecosystems decentralise, traditional information retrieval must be revisited while standards for new techniques and system designs are developed to ensure efficient, accurate, and privacy-preserving search. The Second DESERE workshop at CIKM 2025 focuses on infrastructures and retrieval algorithms for user-controlled data. It convenes a cross-disciplinary community spanning data retrieval, management and integration, semantic technologies, recommendation systems, privacy-aware computing, and search efficiency to explore approaches that prioritize user agency, data ownership, and scalable retrieval across PODs and related architectures. Through paper presentations, panels, and interactive sessions, the workshop will highlight challenges, opportunities, and solutions for privacy-preserving IR. These discussions are especially relevant to domains where user-centric design and data stewardship are critical-such as personal finance, education, and high-stakes areas like criminal justice and health.
Academic departments frequently exchange exam materials, assignment submissions, grade reports, attendance logs and durable student records. Conventional Learning Management Systems (LMS) centralize control and provide limited, non-portable audit trails which hinders cross-department collaboration and independent verification. This paper defines Electronic Student Records as encrypted, content-addressed documents anchored on Ethereum while data remain off chain and presents a practical model with a comparative analysis for department-level sharing. The model uses Sign-In with Ethereum (SIWE) for authentication, verifiable credentials for roles and consent, a minimal on-chain registry that records Keccak-256 digests, content identifiers, and timestamps with a policy reference and InterPlanetary File System (IPFS) storage with institutional pinning and multiple gateways. Access control adopts proxy re-encryption, enforced with time windows and periodic key rotation. Updates always re-encrypt and re-upload so each version receives a new Content Identifier (CID) and digest. A compact evaluation contrasts Ethereum Layer 1 with Zero-Knowledge (ZK) rollups for registry events, single versus multi-gateway IPFS with two or more replicas and Proxy Re-Encryption (PRE) versus Attribute-Based Encryption (ABE) as alternative keying strategies. The system reports metrics include chain cost, one-gateway (p50) and two-gateway (p95) read latency, availability under replication and revocation time under the re-upload policy. Findings indicate ZK rollups suit routine registry events, Layer1 suits high-value anchors, multi-gateway pinning improves tail latency and success rate and time-bounded PRE enables low-overhead revocation. The model offers a feasible path to privacy-preserving, auditable Electronic Student Records (ESRs) that integrate with existing LMS.
This paper studies the aspects of Distributed Ledger Technology (DLT) related to deciding how to order incoming ledger data records under the requirement to provide transparency and security in public information systems, particularly in the context of e-government. The study addresses the challenges of synchronizing ledger replicas across distributed systems, ensuring consistency and reliability in data storage and retrieval. The research highlights the importance of maintaining a unified ledger state, which is crucial for decisionmaking processes in the public and social sectors. The proposed solution combines Lamport's clock algorithm with wave algorithms to overcome the limitations of traditional timestamping methods, such as the duplication of timestamps for concurrent events. This approach ensures that each event in the distributed ledger is uniquely identified, thereby enhancing the system's robustness and efficiency.
In increasingly dynamic organizational and computational environments, workflows controlled by multi-agent systems face often face context changes, blurred task borders, and unexpected behaviors. This research proposes a new model for coping with decision drift in adaptive workflows by modeling latent boundary negotiation. By using concepts from complex systems theory, cognitive modeling, and distributed artificial intelligence, we define agents as autonomous systems which are able to perceive, report, and re-negotiate internal role expectations through localized negotiation protocols. The designed model merges drift detection with multi-agent coordination techniques and correlates them to simulated workflow processes typical for high risk, time constrained settings like crisis management, distributed logistics, and agile development teams. Simulation results show that latent negotiation improves task coherence, decreases agent interference, and stabilizes the performance of workflows under ambiguous and changing boundary conditions much more than negotiation less systems. The benefits of negotiation-aware multi-agent systems in supporting sustained alignment of decisions and flexibility in the system are highlighted through comparison with static and rule-based systems. The results outline latent boundary negotiation as a key feature towards the implementation of intelligent self-organizing work systems in decentralized multi agent ecosystems.
The design of Web3 applications presents unique challenges due to their complex technical requirements. Despite the increasing spread of this technology, there is a notable lack of comprehensive, empirically grounded design guidelines for developing user-friendly Web3 interfaces. This study addresses this gap through a systematic three-phase approach: (1) developing initial guidelines from a literature review and industry sources (n = 31), (2) conducting evaluations using a 14-point framework based on the initial guidelines to test its effectiveness across diverse Web3 applications (n = 25), and (3) validating refined guidelines through expert evaluation sessions (n = 7). Expert evaluations highlighted the need for task-oriented rather than category-based organization of design principles. Based on these findings, we developed a structured framework organizing guidelines into four key task flows, each with three implementation levels. The framework emphasizes progressive disclosure of blockchain concepts, integrated user education, and clear state visualization. Our findings contribute to academic discussion and industry practice by providing empirically validated patterns for Web3 interface design. This study lays a foundation for creating more accessible and user-friendly decentralized applications, though future work should focus on longitudinal validation and adaptation to emerging technologies.
The rapid expansion of remote work has prompted organizations to seek cloud-based solutions that ensure data integrity, robust security, compliance with industry regulations, and optimal performance. Blockchain technology, recognized for its immutable distributed ledger capabilities, presents a compelling value proposition for securing remote workforce management platforms. This paper examines the integration of blockchain technology into cloud-based remote workforce management infrastructures, with a focus on data security, regulatory compliance, and performance optimization. Using hypothetical but realistic enterprise scenarios, real-time data metrics, and visual aids, we demonstrate how blockchain-backed cloud solutions can mitigate security breaches, streamline compliance reporting, and improve operational efficiency. The study concludes tshat blockchainâs decentralized verification mechanisms, combined with the scalability and reliability of modern cloud platforms, can significantly enhance the management of distributed teams while satisfying the evolving demands of data governance.
Mary C. Lacity, Sebastian Schuetz, Le Kuai, Zachary R. Steelman
Trust is one of the most important constructs for understanding the adoption of information technologies (IT). In this paper, we review and analyze two literatures on the construct of human trust in IT artifacts and in the entities that source, operate, and govern IT. The first literature review focuses on defining of the construct of trust across a range of disciplines. Our analysis of this literature identified 13 assumptions about the nature of trust. The assumptions illustrate the complexities of human trust. The second literature review focused on 214 empirical studies of the construct of trust published in the AIS Senior Scholarsâ Basket of Eight journals. We analyze this literature to identify IS scholarâs most common assessments of trust from qualitative studies and most common measures of trust from quantitative studies. As a cumulative body of knowledge, IS scholars have deeply examined the multidimensional aspect of trust by examining different types of trust, including affective trust, cognitive trust, institutional trust, instrumental trust, intrinsic trust, knowledge-based trust, relational trust, swift trust, disposition to trust, trusting beliefs, and more. IS scholars have also extensively examined the assumption that trust is dynamic, as evidenced by the many qualitative papers that examined trust as a process. Our review also finds that IS scholars have conducted extensive research examining trust in Web2 technologies, which are characterized by centralized applications and centralized governance. While the IS scholarly community has established a substantial tradition around the construct of trust, there is still interesting work to be done. With recent releases of open generative AI and with the rise of Web3 technologies like blockchains that purport to be âtrustless,â the construct of trust in IT needs to be re-examined in these emerging contexts. We also encourage more research on trust in bi-directional relationships, on the limits of transitive trust, and on the construct of distrust.
This article explores the importance of putting users at the center of consent processes, in Single Sign On (SSO) systems to tackle privacy issues and empower user independence. It dives into the world of SSO systems shedding light on their privacy weaknesses and the need for users to have control over how their data is shared. By looking at privacy focused SSO solutions and their drawbacks the article suggests a plan to give users control over their data sharing preferences during authentication. The main elements of this plan include a user consent management interface, consent choices, educational materials, preference persistence and tracking logs. Additionally it talks about the obstacles in implementing consent driven SSO systems like creating consent APIs and incorporating privacy boosting technologies such as zero knowledge proofs and decentralized identity frameworks. By tackling these hurdles and promoting designs that prioritize users the article aims to help create authentication solutions that prioritize privacy in line, with changing regulations and user desires.
Jorge Saldivar, Elena MartĂnez-Vicente, David Rozas, MarĂa Cruz Valiente ¡ 5 authors
Upon its arrival, the Ethereum blockchain promised to introduce a new paradigm of Internet-based applications that would revolutionize multiple fields, from finance to IoT to the public sector. Until now, scientific efforts have been primarily focused on theoretical discussions about the implications of the technology and on technical proposals to improve and consolidate the underlying infrastructure, neglecting the experience of people using blockchain-based systems. However, for this technology to permeate the mainstream, blockchain technology should be easily accessible to the general public. This paper reports on evaluations conducted with first-time blockchain users of two Internet-mediated communities using prototype applications built on Ethereum. Results unveil that even users familiar with technology experienced severe difficulties using blockchain-based apps. Also, we saw how blockchain metaphors and transaction-mediated interactions challenge established mental models for modern applications, imposing heavy workloads on users. We conclude the paper by discussing design implications resulting from blockchainâs paradigm change.
Part of the recent developments in Ubiquitous Music (ubimus) research involve the proposal of the Internet of Musical Stuff (IoMuSt) as an expansion and complement to the Internet of Musical Things (IoMusT). The transition from IoMusT to IoMuSt entails a critique of blockchain and non-fungible tokens (NFTs) as technologies for allotment, disciplination and regimentation of formerly open and freely accessible artistic web content. In brief, the replacement of the operative concepts constructed around âthingsâ with strategies based on âstuff â highlights the underlying interconnected processes and factors that impact interaction and usage, pointing to resources that become disposable and valueless within an objectified and monetized musical internet. This conceptual and methodological turn allows us to deal with distributed-creativity phenomena in marginalized spaces, highlighting the role of resources that are widely reproducible, fluid and ever-changing. In this paper, we address IoMuSt-based responses to issues such as the artificial production of scarcity associated with the application of NFTs. The selected musical examples showcase the meshwork of dynamic relationships that characterizes ubimus research. In particular, we focus on a comprovisation project involving VOIP visual communication through Skype, Meet and Zoom, a ubimus experience involving a Telegram chatbot and a set of musical experiments enabled by an online tool for remote live patching.
During the global pandemic, information workers were abruptly forced to engage in virtual work. This paper reports from an experiment seeking to formalize the formalization of small team coordination at London Blockchain Lab through the use of blockchain supported tokenization. The Web3 organizing vision promotes the technology as an enabler of new ways for individuals and organizations to engage in the transparent exchange of scarce digital rights. However, little attention has been paid to the use of blockchain technologies to coordinate distributed collaborative activities. This paper seeks to understand the viability of this vision amongst a community of expected early adopters through design experimentation resulting in interview data. The study points towards the significant gap between the Web3 vision and the problems of realizing this in practice. This highlights fundamental barriers to using blockchain for team collaboration while also pointing toward its potential. Even the most willing and able find it hard to turn code into law through tokenizing collaboration.
Mehmet AydÄąn BaytaĹ, Amos Cappellaro, Ylva Fernaeus
Non-fungible tokens (NFTs) have been a defining trend for design, technology, and business in 2021. The value, legitimacy, and utility of NFTs is disputed: proponents highlight revolutionary economic and cultural potentials of an open, secure, and immutable ownership database, while opponents are displeased by the environmental issues and abundant wrongdoing in the ecosystem. Nevertheless, the phenomenon is relevant to HCI, and signifies important developments for future interactive products. To better understand the NFT phenomenon, and to inform future HCI research and design, we investigated the stakeholders in the NFT ecosystem and relations between them. Based on open data we mined from the social news website Hacker News, we contribute the first data-backed model of stakeholders in the NFT ecosystem. The model reveals a nuanced account of the outlooks of creators, owners, and technologists; identifies investment firms and auction houses as arbiters of knowledge and value; and presents implications for future research.