Smart contracts, closely intertwined with cryptocurrency transactions, have sparked widespread concerns about considerable financial losses of security issues. To counteract this, a variety of tools have been developed to identify vulnerability in smart contract. However, they fail to overcome two challenges at the same time when faced with smart contract bytecode: (i) strong interference caused by enormous non-relevant instructions; (ii) missing semantics of bytecode due to incomplete data and control flow dependencies. In this paper, we propose a multi-teacher based bytecode vulnerability detection method, namely Multi-Teacher Vulnerability Hunter (MTVHunter), which delivers effective denoising and missing semantic to bytecode under multi-teacher guidance. Specifically, we first propose an instruction denoising teacher to eliminate noise interference by abstract vulnerability pattern and further reflect in contract embeddings. Secondly, we design a novel semantic complementary teacher with neuron distillation, which effectively extracts necessary semantic from source code to replenish the bytecode. Particularly, the proposed neuron distillation accelerate this semantic filling by turning the knowledge transition into a regression task. We conduct experiments on 229,178 real-world smart contracts that concerns four types of common vulnerabilities. Extensive experiments show MTVHunter achieves significantly performance gains over state-of-the-art approaches.
• Security challenges in RFID, edge computing and blockchain enabled supply chains are analysed, focusing on authentication. • A lightweight mutual authentication protocol is proposed for RFID-based supply chains in edge computing and blockchain systems. • The protocol uses XOR, bitwise rotation, and hash functions to ensure security with minimal computational overhead. • Formal security analysis is performed using the Real-Or-Random (ROR) model and validation with the AVISPA tool. • The protocol is adaptable for various supply chains and customizable to meet different IIoT security needs. This paper addresses security challenges, especially in the authentication mechanism of Industrial Internet of Things (IIoT)-enabled supply chain systems by proposing an enhanced Radio Frequency Identification (RFID) authentication protocol. The current system faces significant security risks due to increased connectivity and data exchange within supply chain networks. The proposed protocol integrates edge computing and blockchain to ensure secure, efficient mutual authentication between RFID tags and supply chain nodes. By utilizing the real-time processing capabilities of edge computing and the decentralization and immutability of blockchain, the protocol enhances the security of data transmitted in the system. The proposed protocol utilizes lightweight cryptographic functions optimized for resource-constrained edge devices, ensuring secure authentication and data transmission without compromising scalability or efficiency. Permissioned blockchain technology further strengthens trust and transparency in the supply chain by providing a decentralized, tamper-resistant ledger. The protocol employs cryptographic techniques such as a cryptographically secure one-way hash function, random number generation function, and circular shift operations to ensure data integrity and confidentiality, achieving mutual authentication, forward secrecy, and resistance to cryptographic attacks. Formal security analysis of the proposed authentication protocol is performed using the Real-Or-Random (ROR) model. The results demonstrate that the protocol offers superior trade-offs in term of security, computational cost, and communication efficiency compared to existing authentication protocols in this field. Simulation of the protocol is performed using Automated Validation of Internet Security Protocols and Applications (AVISPA) tools. Its lightweight design makes it suitable for real-world application in resource-constrained IIoT environments.
This study aims to see the development of research on the topic of "Islamic Finance & Cryptocurrency" and research plans that can be carried out based on journals published on the theme. This research uses a qualitative method with a bibliometric analysis approach. The data used is secondary data with the theme "Islamic Finance & Cryptocurrency" which comes from the Scopus database with a total of 40 journal articles. Then, the data is processed and analyzed using the R-Biblioshiny and VosViewer applications with the aim of knowing the bibliometric map of "Islamic Finance & Cryptocurrency" research development in the world. The results of the study found that there are 4 clusters with the most used words are cryptocurrencey, islamic finance, blockchain technology, money, bitcoin, fintech, digital currency, and islamic crypto assets. Then, the research path topics related to Islamic Finance & Cryptocurrency are (1)Blockchain Adoption in Islamic Finance, (2)Digital Currency Transactions in Islam, (3)Potential and Challenges of Islamic Crypto, and (4)Islamic Law Implications on Cryptocurrency.
Abstract Smart agriculture promises to alleviate the burden of climate risks on crop production by leveraging machine learning tasks. These tasks act as a decision support instrument for making well-informed choices by stakeholders in the agricultural value chain. Currently, predictive models in smart agriculture demand a centralized collection of diverse data, fragmented across multiple information systems leading to a single point of failure. The application of the Federated Learning (FL) technique restricts the movement of raw data and trains the model at the data source. However, the FL approach does not ensure trust factors like privacy, authentication, data provenance, transparency and traceability. To address this, a decentralized federated learning framework built on blockchain can be a potential solution. In this study, we introduce a blockchain-based framework called AgriFLChain for trusted federated learning in the context of smart agriculture. We focus on crop yield prediction as an illustrative use case, initially discussing centralized deep learning models (ResNet-16, ResNet-28, CNN-DNN, and CNN-LSTM). We then detail the authentication and data provenance mechanisms for federated learning participants, utilizing blockchain-based Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). We implement these models using vanilla federated learning and Differential Privacy (DP) federated learning approaches, achieving transparency and traceability through smart contracts by recording metadata of model updates into the blockchain. Finally, detailed evaluation demonstrates that AgriFLChain achieves comparable efficiency to centralized models while maintaining scalability in blockchain transactions for higher data volumes.
Paula Heess, Stefanie Holly, Marc-Fabian Körner, Astrid Nieße · 9 authors
Abstract The need to harness the flexibility of small-scale assets for system stabilization, including redispatch, is growing rapidly with the increasing prevalence of distributed generation, such as photovoltaic systems and heavy loads, in particular heat pumps and electric vehicles. Integrating these resources into the redispatch process presents special requirements: On the one hand, building trust with the owners of such assets requires privacy and a reasonable degree of autonomy and engagement. On the other hand, besides the system’s scalability and robustness, the verifiability and traceability of provided data are essential for grid operators who depend on the reliable provision of redispatch services. To date, research and practice have encountered significant challenges in defining a system that enables the inclusion of decentralized flexibilities while satisfying necessary requirements. To that end, we present a novel conceptual system design that addresses these challenges by combining a multi-agent system (MAS) approach with verifiable information flows through digital self-sovereign identities (SSIs) and Zero-Knowledge-Proofs (ZKPs). Single agents, as edge devices, operate locally and autonomously, respecting customer preferences, while MAS provide the ability to design robust, reliable, and scalable systems. SSI enables agents to manage their data autonomously, while ZKPs are used to protect users’ privacy through selective data disclosure which allows the verification of the correctness of information without disclosing the underlying data. To validate the feasibility of this design, a case study is included to demonstrate the functionality of key sub-processes, such as baseline optimization, aggregation, and disaggregation, in a realistic scenario. This case study, supported by a prototype implementation, provides initial evidence of the concept’s soundness and lays the groundwork for future evaluation through extensive simulations and field testing. Together, the technologies included in the conceptual system design balance full transparency for grid operators with autonomy and data economy for asset owners.
Social media significantly impacts the dynamics and move-ment of stock and cryptocurrency markets. The perfect market hypothesis assumes that all available information is already embed-ded in stock prices, meaning that only some announcements will affect the market price of a company’s stock. The efficient market hypothesis and anomalies that cause certain deviations apply to both stock and cryptocurrency markets. Social networks like Twitter, Facebook, and Reddit are significant tools for promoting cryptocurrencies. This paper aims to explore the influence of social media on market price move-ments, using Tesla stock and Bitcoin as examples. The conclusion is that Musk’s Tweet Effect demonstrates an anomaly of market over-reaction and impacts the price movements of Bitcoin and Tesla stocks.
The rapid growth of decentralized finance (DeFi) has provided numerous benefits, but it has also presented significant economic security challenges. One of the most critical issues is Maximum Extractable Value (MEV). MEV refers to the opportunities for miners or validators to earn additional profits by altering the order of transactions. However, current MEV detection methods have notable limitations. These include poor adaptability of algorithms, the vastness of the search space, and the inefficiency of methods that rely on traditional heuristic approaches. To overcome these challenges, we introduces a reinforcement learning-based MEV optimization system for blockchain—RL-BES (Reinforcement Learning for Blockchain Economic Security). This system employs two deep reinforcement learning networks to optimize transaction ordering and template parameters, integrated with Monte Carlo Tree Search (MCTS) for effective path exploration. Furthermore, we presents a custom model evaluation tool designed to adjust various networks and parameters, facilitating the analysis of the best algorithmic solutions for on-chain MEV extraction. Experimental results indicate that the RL-BES system excels in multiple DeFi applications. It demonstrates faster convergence and consistently surpasses the performance of Flashbot and other similar detection tools.
The integration of Internet of Things (IoT) devices in healthcare has revolutionized patient care by enabling real-time monitoring, personalized treatments, and efficient data management. However, this technological advancement introduces significant security risks, particularly concerning the confidentiality, integrity, and availability of sensitive medical data. Traditional security measures are often insufficient to address the unique challenges posed by IoT environments, such as heterogeneity, resource constraints, and the need for real-time processing. To tackle these challenges, we propose a comprehensive three-phase security framework designed to enhance the security and reliability of IoT-enabled healthcare systems. In the first phase, the framework assesses the reliability of IoT devices using a reputation-based trust estimation mechanism, which combines device behavior analytics with off-chain data storage to ensure scalability. The second phase integrates blockchain technology with a lightweight proof-of-work mechanism, ensuring data immutability, secure communication, and resistance to unauthorized access. The third phase employs a lightweight Long Short-Term Memory (LSTM) model for anomaly detection and classification, enabling real-time identification of cyber threats. Simulation results demonstrate that the proposed framework outperforms existing methods, achieving a 2% increase in precision, accuracy, and recall, a 5% higher attack detection rate, and a 3% reduction in false alarm rate. These improvements highlight the framework's ability to address critical security concerns while maintaining scalability and real-time performance.
In this era, significant transformations in industries and tool utilization are driven by AI/Large Language Models (LLMs) and advancements in Machine Learning. There's a growing emphasis on Machine Learning Operations(MLOps) for managing and deploying these AI models. Concurrently, the imperative for richer smart contracts and on-chain computation is escalating. Our paper introduces an innovative framework that integrates blockchain technology, particularly the Cosmos SDK, to facilitate on-chain AI inferences. This system, built on WebAssembly (WASM), enables interchain communication and deployment of WASM modules executing AI inferences across multiple blockchain nodes. We critically assess the framework from feasibility, scalability, and model security, with a special focus on its portability and engine-model agnostic deployment. The capability to support AI on-chain may enhance and expand the scope of smart contracts, and as a result enable new use cases and applications.
Especially new generation investors may prefer to use stocks of popular companies that use advanced technologies and cryptocurrencies as investment instruments. Gold, one of the classical investment instruments, still maintains its place among the commodity assets in the portfolios of investors around the world. These asset groups were evaluated in this study. As the first group investment tool, decacorn and hectocorn technology companies called the new generation the magnificent five; Company stock returns of Apple, Microsoft, Amazon, Alphabet, Nvidia Corporation and Tesla were analyzed. In addition, as the second financial asset, cryptocurrencies, which are used as investment instruments as well as being used in daily life with the evolution of technology, and Bitcoin (BTC), which remains popular among these cryptocurrencies, were the subject of the study. Finally, the study evaluated gold mines, one of the world's oldest valuable investment instruments, compared with other financial assets. The study examined the magnificent five stocks, BTC and gold ounce prices between the periods of 2020:01 and 2023:12, using mutual cointegration, vector error correction (VEC) and Granger causality analyses. Findings of the study; Short-term shocks caused by variables in BTC stabilise after about a month. In this process, as NVDA shares increase, BTC value decreases, and as gold value increases, BTC value increases.
The orchestration of agents to optimize a collective objective without centralized control is challenging yet crucial for applications such as controlling autonomous fleets, and surveillance and reconnaissance using sensor networks. Decentralized controller design has been inspired by self-organization found in nature, with a prominent source of inspiration being flocking; however, decentralized controllers struggle to maintain flock cohesion. The graph neural network (GNN) architecture has emerged as an indispensable machine learning tool for developing decentralized controllers capable of maintaining flock cohesion, but they fail to exploit the symmetries present in flocking dynamics, hindering their generalizability. We enforce rotation equivariance and translation invariance symmetries in decentralized flocking GNN controllers and achieve comparable flocking control with 70% less training data and 75% fewer trainable weights than existing GNN controllers without these symmetries enforced. We also show that our symmetry-aware controller generalizes better than existing GNN controllers. Code and animations are available at http://github.com/Utah-Math-Data-Science/Equivariant-Decentralized-Controllers.
Personal AI assistants (e.g., Apple Intelligence, Meta AI) offer proactive recommendations that simplify everyday tasks, but their reliance on sensitive user data raises concerns about privacy and trust. To address these challenges, we introduce the Guardian of Data (GOD), a secure, privacy-preserving framework for training and evaluating AI assistants directly on-device. Unlike traditional benchmarks, the GOD model measures how well assistants can anticipate user needs-such as suggesting gifts-while protecting user data and autonomy. Functioning like an AI school, it addresses the cold start problem by simulating user queries and employing a curriculum-based approach to refine the performance of each assistant. Running within a Trusted Execution Environment (TEE), it safeguards user data while applying reinforcement and imitation learning to refine AI recommendations. A token-based incentive system encourages users to share data securely, creating a data flywheel that drives continuous improvement. Specifically, users mine with their data, and the mining rate is determined by GOD's evaluation of how well their AI assistant understands them across categories such as shopping, social interactions, productivity, trading, and Web3. By integrating privacy, personalization, and trust, the GOD model provides a scalable, responsible path for advancing personal AI assistants. For community collaboration, part of the framework is open-sourced at https://github.com/PIN-AI/God-Model.
Yang Zhou, Chi Xie, Gang‐Jin Wang, Jue Gong · 5 authors
Abstract Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways. Its increasingly complex interactions with the conventional financial market make precisely forecasting its volatility increasingly challenging. To this end, we propose a novel framework based on the evolving multiscale graph neural network (EMGNN). Specifically, we embed a graph that depicts the interactions between the cryptocurrency and conventional financial markets into the predictive process. Furthermore, we employ hierarchical evolving graph structure learners to model the dynamic and scale-specific interactions. We also evaluate our framework’s robustness and discuss its interpretability by extracting the learned graph structure. The empirical results show that (i) cryptocurrency volatility is not isolated from the conventional market, and the embedded graph can provide effective information for prediction; (ii) the EMGNN-based forecasting framework generally yields outstanding and robust performance in terms of multiple volatility estimators, cryptocurrency samples, forecasting horizons, and evaluation criteria; and (iii) the graph structure in the predictive process varies over time and scales and is well captured by our framework. Overall, our work provides new insights into risk management for market participants and into policy formulation for authorities.
This paper explores and compares two participatory management approaches—the Company Democracy Model and Holacracy—for their application within the Indian Ministry of Education. It emphasizes the need for innovative organizational techniques in the management of the public sector, particularly in light of the dynamic demands posed by the New Education Policy (NEP) 2020. The study evaluates how these approaches enhance employee engagement and improve the quality of deliverables. Lewin’s Field Force Analysis is utilized to examine the organization’s strategy. The study employs Kotter’s Change Model to assess the applicability of Holacracy—a decentralized, project-oriented system, characterized by its dynamic and self-organizing structures. This model is analyzed for its potential to meet the Ministry’s shifting priorities and to foster adaptability through autonomous teams. Conversely, the Company Democracy Model, which emphasizes employee-centric growth and decision-making within a tiered, spiral framework, is evaluated using the ADKAR Change Model. This model’s compatibility with the Ministry’s hierarchical structure and its potential to enhance participatory governance are key areas of focus. The study contributes novel insights by integrating change management theories with a refined presentation of the CDM pyramid and by introducing specific performance metrics for both models. By combining theoretical frameworks with practical applications, this paper offers a sustainable governance model suited to dynamic organizational environments.
Distributed ledger technology (DLT) enable the immutable, transparent, and tamper-proof storage of transaction records in a shared electronic register. Due to these intrinsic properties, DLT has the potential to be highly disruptive to businesses. However, the heterogeneity and intricacy of the underlying technologies impede their more widespread adoption. A primary challenge lies in comprehending the interrelationships between the organizational, economical, and technical dimensions of DLT systems. An integrated perspective on the fundamental concepts of DLT within each dimension can be accomplished through the implementation of domain-specific conceptual modeling languages that explicitly account for intrinsic properties. Conceptual models adhere to well-defined elements and rules for the utilization of these elements. This enables the algorithmic processing of models to generate derivations and new insights.The focus of prior research on modeling support for DLT has been on the technical dimension. However, research on conceptual modeling approaches enabling the joint consideration of organizational, economical, and technical factors is lacking. This situation is undesirable both from a scientific standpoint and from a practical one. To address this gap, this thesis advances the current state-of-the-art in the design of DLT applications by introducing several domain-specific approaches addressing concerns across dimensions. This includes support for the decision-making in organizations, for the design of their business model, its alignment with the underlying DLT systems, and for the development of smart contracts and subsequent generation of functional code. The artifacts are consolidated into a domain-specific conceptual modeling method, facilitating the design of DLT applications and business cases.
Order fairness in distributed ledgers refers to properties that relate the order in which transactions are sent or received to the order in which they are eventually finalized, i.e., totally ordered. The study of such properties is relatively new and has been especially stimulated by the rise of Maximal Extractable Value (MEV) attacks in blockchain environments. Indeed, in many classical blockchain protocols, leaders are responsible for selecting the transactions to be included in blocks, which creates a clear vulnerability and opportunity for transaction order manipulation. Unlike blockchains, DAG-based ledgers allow participants in the network to independently propose blocks, which are then arranged as vertices of a directed acyclic graph. Interestingly, leaders in DAG-based ledgers are elected only after the fact, once transactions are already part of the graph, to determine their total order. In other words, transactions are not chosen by single leaders; instead, they are collectively validated by the nodes, and leaders are only elected to establish an ordering. This approach intuitively reduces the risk of transaction manipulation and enhances fairness. In this paper, we aim to quantify the capability of DAG-based ledgers to achieve order fairness. To this end, we define new variants of order fairness adapted to DAG-based ledgers and evaluate the impact of an adversary capable of compromising a limited number of nodes (below the one-third threshold) to reorder transactions. We analyze how often our order fairness properties are violated under different network conditions and parameterizations of the DAG algorithm, depending on the adversary's power. Our study shows that DAG-based ledgers are still vulnerable to reordering attacks, as an adversary can coordinate a minority of Byzantine nodes to manipulate the DAG's structure.
Dee Hock, the founder of Visa, coined the term 'chaordic' to describe simultaneously chaotic and ordered systems. Based on his reasoning, we introduce the Theory of Chaordic Economics to explain how economic systems are transformed by two disruptive technologies: namely Artificial Intelligence and Blockchain. Artificial intelligence can generate novel output through algorithmic yet rather unpredictable processes. Blockchain creates deterministic results without central authorities and relies on elaborated protocols that prescribe how consensus can be reached within a network of peers. The amalgamation of chaos and order produces chaordic economic systems and can yield hitherto unthinkable economic structures.
The server-less nature of Decentralized Federated Learning (DFL) requires allocating the aggregation role to specific participants in each federated round. Current DFL architectures ensure the trustworthiness of the aggregator node upon selection. However, most of these studies overlook the possibility that the aggregating node may turn rogue and act maliciously after being nominated. To address this problem, this paper proposes a DFL structure, called TrustChain, that scores the aggregators before selection based on their past behavior and additionally audits them after the aggregation. To do this, the statistical independence between the client updates and the aggregated model is continuously monitored using the Hilbert-Schmidt Independence Criterion (HSIC). The proposed method relies on several principles, including blockchain, anomaly detection, and concept drift analysis. The designed structure is evaluated on several federated datasets and attack scenarios with different numbers of Byzantine nodes.
Abdourahmane Diallo, Akhtarzaite Binti Hj. Abdul Aziz
This article examines whether zakat is applicable to cryptocurrencies via a study of the fatwa genre on this topic. Fatwas from seven legal specialists in the Muslim world will be examined. These juristic opinions range from considering cryptocurrencies to be permissible to those that consider it prohibited. However, across this spectrum these authorities argue for zakat application to cryptocurrencies. The analysis will isolate and identify the relevant proof texts and principles relied upon. It will be shown that a unifying theme across the fatwa genre is of how thamaniyya is vital in linking cryptocurrencies to zakat-applicability. The study concludes that Islamic Finance successfully contains a theoretic concept, namely thamaniyya, that can help Muslim scholarship engage with advancements in cryptocurrencies regardless of future technological innovations. This study is novel for focusing on fatwa analysis through a theoretical orientation. This article sheds light on how contemporary fatwas are used in the field of Islamic Finance to negotiate between the jurisprudential tradition and cutting-edge developments in cryptocurrencies. The study is limited in not considering socio-political factors in the analysis. It is hoped the results of this study can highlight how disparate legal opinions in Islamic Finance actually share common ground.
This research introduces FoReChain (Fog-based Re-Encryption Chain), a blockchain-enabled proxy re-encryption (PRE) framework designed for secure, low-latency data sharing in fog-based IoT networks. The framework addresses key challenges related to data security, privacy, and performance in distributed environments, where traditional models face issues like high latency, limited scalability, and inefficient key management. FoReChain integrates blockchain with ECC-based proxy re-encryption to secure data without exposing original content. A delegated Practical Byzantine Fault Tolerance (d-PBFT) consensus mechanism ensures efficient transaction validation. The framework processes data at fog nodes, reducing delays commonly found in cloud-dependent models. Key management relies on time-based key updates stored immutably on the blockchain, while zero-knowledge proofs support secure, anonymous data sharing. The study evaluates FoReChain against FE-PRE and PREA models using metrics such as latency, throughput, scalability, blockchain consensus time, and adaptive policy effectiveness. Results show lower latency, higher throughput, and better adaptability in FoReChain, especially under heavy network conditions like smart healthcare and industrial IoT setups. FoReChain demonstrates secure data sharing, efficient resource utilization, and reliable key management in dynamic IoT environments. It offers consistent performance under varying loads, with improved scalability and data integrity maintained through decentralized validation.
Steve Springer Laryea, Kofi Agyarko Ababio, Jules Clément, Marno Booyens
This paper aims to investigate investors’ prospects in adding value to their portfolios by considering investors’ behavioural score (Cumulative Prospect Theory (CPT) score) and a clustering technique in the selection of assets. The universe of assets constitutes 63 cryptocurrencies sourced from Bloomberg from Jan 01, 2020, to July 31, 2022. The study period was segmented into two distinct and mutually exclusive periods, namely COVID-19, and post-COVID-19. Nine portfolios were constructed of which six were based on the CPT and the remaining on the K means Clustering technique. Using the copula-based Differential Evolution (DE) algorithm for the optimisation, the results show that portfolios consisting of assets with extremely high CPT scores were preferred during the post-COVID-19 and full sample periods, except for portfolios comprising assets with extremely low CPT scores during the COVID-19 period. The most optimised portfolio was composed of classified assets with extremely high CPT scores in the post-COVID-19 period. These findings provide intuitive and coherent investment strategies to guide investors in the cryptocurrency market.
Gabriel A. Giménez Roche, Antoine Noël, Loïc Sauce
We analyze the determinants of Bitcoin (BTC) trade volume in decentralized exchanges (DEXs) and test the claim that BTC trades on these platforms are censorship-resistant. The study finds that overall economic freedom, particularly monetary freedom, correlates indirectly with BTC trade volumes, while capital restrictions on residents' transactions abroad correlate in two different directions. Purchase transactions inversely correlate with BTC volume in DEXs, while sales transactions correlate directly. These results suggest that BTC can be used to hedge against poor institutional frameworks, particularly against poor monetary governance, and as a vehicle for institutional hedging against repressive capital controls and institutional failures. The study's originality lies in its use of on-chain panel data on the volume of BTC transactions, which are country-specific and allow for comparing the impact of country-specific socio-institutional variables on BTC volumes. • Decentralized exchanges leverage blockchain for innovative financial services. • BTC provides an institutional hedging option against poor governance frameworks. • On-chain data reveal BTC country dynamics and institutional hedging potential.
ABSTRACT Blockchain technologies have catalyzed the rise of decentralized autonomous organizations (DAOs), which operate in an incentive network fueled by crypto tokens. In essence, these tokens are imbued with either payment rights (i.e., transactional tokens) or ownership rights (i.e., governance tokens). The decentralized organizational paradigm dismantles the traditional management structure and bring new research opportunities to Operations Management (OM). While the performance of DAOs has been largely examined in current OM literature, the effectiveness of their internal incentive mechanisms—specifically the one that uses ownership as rewards to promote user contributions—remains unclear. Focusing on DAO‐enabled virtual communities, we seek to examine whether decentralized ownership provides stronger incentives for user behaviors, such as creation and curation, in comparison to traditional monetary rewards through the lens of psychological ownership theory. We obtained data from Steemit that captures the reward, creation, curation and transaction behaviors of 98,000 users from May 2017 to April 2019. By leveraging the “power‐up” action as a shock that increases user ownership shares, we established a quasi‐experimental setting. Employing the PSM‐DID model, we found that the use of governance tokens is associated with enhanced creation and curation efforts but declined creation novelty, compared to the use of transactional tokens. Our additional analyses further reveal that the incentive effects of governance tokens diminish over time. However, upon the recurrence of the intended choice, these effects become reinforced. Notably, we find that governance token ownership is more strongly associated with curation efforts for users with weaker social ties. Conversely, for users with high reputation scores, their content creation behaviors are less strongly associated with governance token ownership. This study contributes to the burgeoning discourse on blockchain and cryptocurrency from an operational perspective, providing valuable insights for the design of incentive mechanisms in DAOs and advancing our understanding of operational efficiencies and stakeholder engagement in decentralized structures within Operations Management.
Hossein Naderi, Alireza Shojaei, Mohammad Hossein Heydari
Building energy efficiency programs face significant challenges in performance monitoring and occupant engagement, which hinder the achievement of sustainability goals in the built environment. Traditional systems often suffer from intermediary-dependent workflows, insufficient transparency, and reliability issues, leading to conflicts among stakeholders and reduced occupant participation. This study proposes a blockchain-enabled solution that leverages Non-Fungible Tokens (NFTs) to improve the transparency, reliability, and traceability of performance monitoring systems. By integrating Digital Twin (DT) technology, blockchain, and a token marketplace, the platform not only enhances monitoring capabilities but also incentivizes occupants to adopt energy-efficient behaviors through Fungible Token (FT) rewards. A proof-of-concept prototype was developed using a synthetic case study, demonstrating the feasibility, cost efficiency, and scalability of the framework. The findings emphasize the importance of network selection for wider blockchain adoption. This transparent and immutable framework addresses key challenges in energy performance monitoring, offering a foundation for advancing sustainability in the built environment.