Search Results (1,146)

Blockchain is widely promoted as a tool for enhancing transparency, trust, and sustainability in business, yet little is known about how creative micro, small, and medium enterprises (MSMEs) in emerging economies can meaningfully adopt it for finance and accounting purposes in times of global uncertainty. This study explores how blockchain can be harnessed for transparent and sustainable accounting in Indonesian creative MSMEs amid rapid digital disruption. Using an exploratory qualitative design, we conducted semi-structured, in-depth interviews with 18 owners and key decision-makers across diverse creative subsectors and analysed the data thematically through an integrated Technology Acceptance Model (TAM) and Diffusion of Innovation (DOI) lens. The findings show that participants recognise blockchain’s potential benefits for transaction transparency, verifiable records, intellectual property protection, and secure payments, but adoption is constrained by technical complexity, financial constraints, limited digital and accounting capabilities, and perceived regulatory and reputational risks. Government initiatives are seen as important for legitimacy yet insufficient without concrete guidance, capacity-building, and financial support. The study extends TAM–DOI applications to blockchain-enabled accounting in creative MSMEs and highlights the need for sequenced, ecosystem-based interventions to translate blockchain’s technical promise into accessible, ESG- and SDG-oriented accounting solutions in the creative economy. Full article

Agricultural marketing increasingly integrates Agriculture 4.0 technologies—Blockchain, AI/ML, IoT, and recommendation systems—yet systematic evaluations of computational maturity and deployment readiness remain limited. This Systematic Literature Review (SLR) examined 99 peer-reviewed studies (2019–2025) from Scopus, Web of Science, and IEEE Xplore following PRISMA protocols to assess algorithmic performance, evaluation methods, and Technology Readiness Levels (TRLs) for agricultural marketing applications. Hybrid recommendation systems dominate current research (28.3%), achieving accuracies of 80–92%, while blockchain implementations (15.2%) show fast transaction times (<2 s) but limited real-world adoption. Machine learning models using Random Forest, Gradient Boosting, and CNNs reach 85–95% predictive accuracy, and IoT systems report >95% data transmission reliability. However, 77.8% of technologies remain at validation stages (TRL ≤ 5), and only 3% demonstrate operational deployment beyond one year. The findings reveal an “efficiency paradox”: strong technical performance (75–97/100) contrasts with weak economic validation (≤20% include cost–benefit analysis). Most studies overlook temporal, geographic, and economic generalization, prioritizing computational metrics over implementation viability. This review highlights the persistent validation–deployment gap in digital agriculture, urging a shift toward multi-tier evaluation frameworks that include contextual, adoption, and impact validation under real deployment conditions. Full article

This article presents a comprehensive bibliometric analysis of the indexed academic literature on the application of distributed ledger technology (DLT) and blockchain in the tourism industry. Using the bibliometrix library within the RStudio environment, key bibliometric indicators were examined in order to characterize the evolution, structure, and thematic focus of this emerging field of research. The systematic literature review, which adhered to PRISMA guidelines, involved retrieving publications from the Web of Science and Scopus databases. A curated dataset of 100 relevant documents was identified and analyzed in terms of annual scientific production, leading journals, influential authors, and highly cited publications. The results indicate that blockchain technology dominates the literature, with a strong emphasis on its potential to enhance trust, transparency, and efficiency in tourism-related processes. In particular, identity management, secure transactions, and disintermediation emerge as central research themes, reflecting blockchain’s capacity to support decentralized, immutable, and privacy-preserving interactions between tourists and service providers. Overall, the findings reveal a rapidly growing and increasingly structured body of knowledge, highlighting emerging research directions and technological challenges for future studies on DLT applications in tourism. Full article

The diffusion of intelligent construction technologies has improved construction efficiency and information integration, while also increasing the complexity and uncertainty of governance decisions in megaprojects. In particular, selecting an appropriate Engineering Transaction Mode (ETM) under intelligent construction involves multiple conflicting criteria, expert judgments, and loss-averse risk preferences, which are not fully captured by conventional multi-criteria decision-making methods. This study proposes a decision-making model that combines Pythagorean fuzzy sets (PFSs) and prospect theory to support ETM selection for megaprojects under intelligent construction. The model constructs an ETM evaluation system grounded in a systematic literature review and questionnaire evidence, encodes expert judgments using PFSs, determines expert and criterion weights via information-utility and fuzzy-entropy measures, and aggregates perceived gains and losses relative to positive and negative ideal solutions through prospect theory. A mega-pumping station project with four ETM alternatives is used for validation. Results indicate that “Self-management + Network-based integrated application + Consultant assistance” achieves the highest prospect value and is consistently ranked first; the same ordering is obtained using TOPSIS and a fuzzy comprehensive evaluation method, demonstrating robustness. The study contributes to theory by coupling hybrid fuzzy representation with loss-aversion-based behavioral aggregation for ETM governance under intelligent construction and provides practitioners with a transparent, replicable decision tool to support ETM selection in complex, uncertainty-laden megaprojects. Full article

Current research on cryptocurrency dual-offline payment systems has garnered significant attention from both academia and industry, owing to its potential payment feasibility and application scalability in extreme environments and network-constrained scenarios. However, existing dual-offline payment schemes exhibit technical limitations in privacy preservation, failing to adequately safeguard sensitive data such as payment amounts and participant identities. To address this, this paper proposes a privacy-preserving dual-offline payment method utilizing a cryptographic challenge-response mechanism. The method employs zero-knowledge proof technology to cryptographically protect sensitive information, such as the payer’s wallet balance, during identity verification and payment authorization. This provides a technical solution that balances verification reliability with privacy protection in dual-offline transactions. The method adopts the payment credential generation and credential verification mechanism, combined with elliptic curve cryptography (ECC), to construct the verification protocol. These components enable dual-offline functionality while concealing sensitive information, including counterparty identities and wallet balances. Theoretical analysis and experimental verification on 100 simulated transactions show that this method achieves an average payment generation latency of 29.13 ms and verification latency of 25.09 ms, significantly outperforming existing technology in privacy protection, computational efficiency, and security robustness. The research provides an innovative technical solution for cryptocurrency dual-offline payment, advancing both theoretical foundations and practical applications in the field. Full article

The prevalence of online transactions and extensive adoption of credit card payments have contributed to the escalation of credit card cyber fraud in modern society. These trends are propelled by technological advancements, which provide fraudulent actors with more opportunities. Fraudsters exploit victims’ financial vulnerabilities by obtaining illegal access to sensitive credit card information through deceptive means, such as phishing, fraudulent phone calls, and fraudulent SMS messages. This study predicts and detects potential instances of cyber fraud in credit card transactions by employing Machine Learning (ML) techniques, including Decision Tree (DT); Random Forest (RF); Logistic Regression (LR); Support Vector Machine (SVM); K-Nearest Neighbors (KNN); XGBoost; CatBoost; and sampling techniques such as Tomek Link, Synthetic Minority oversampling technique (SMOTE), Edited Nearest Neighbor (ENN), Tomek+ENN, and SMOTE+ENN. To determine the performance of the algorithms in terms of accuracy, precision, recall, F1 score, and ROC-AUC for credit card cyber fraud detection, we conducted a comparative analysis of the extant ML techniques. Full article

In the rapidly evolving digital economy, the expansion of business-to-business e-commerce ecosystems has compelled traditional industries to integrate into digital supply chains to achieve sustainable development. Industrial e-commerce is no longer limited to online transactions but extends to the digital transformation of backend operations. Drawing upon the perspective of the digital business ecosystem, this study investigates how digital supply chain integration, manifested through digital transformation, impacts energy efficiency. By utilizing a panel fixed effects model and advanced text mining techniques on a dataset of 721 listed firms in the resource-intensive sectors of China spanning from 2011 to 2023, this research constructs a novel index to quantify corporate digital maturity based on semantic analysis. The empirical results demonstrate that digital transformation significantly enhances energy efficiency by facilitating optimized resource allocation and data-driven decision making required by modern digital markets. Mechanism analysis reveals that green innovation functions as a pivotal mediator that bridges the gap between digital investments and environmental performance. Furthermore, this relationship is found to be contingent upon corporate social responsibility strategies, ownership structures, and the scale of the firm. This study contributes to the electronic commerce literature by elucidating how traditional manufacturers can leverage digital technologies and green innovation to navigate the twin transition of digitalization and sustainability, offering theoretical implications for platform governance in industrial sectors. Full article

In recent years, global supply chains have become increasingly vulnerable to geopolitical tensions, pandemics, and energy crises, particularly in resource-constrained regions characterized by weak infrastructure and high transaction costs. Using panel data on A-share listed firms in China’s central and western regions from 2010 to 2022, this study examines the effect of firm-level digital transformation on supply chain resilience. We construct a digital transformation index and employ an instrumental-variable approach based on the interaction between terrain ruggedness and lagged digital transformation to address endogeneity concerns. Empirical results show that the digital transformation of enterprises has significantly enhanced the resistance and recovery capabilities of the supply chain, verifying its effectiveness in resource-constrained environments. Mechanism analyses reveal that this effect operates through increased supply chain diversification—especially customer diversification—and improved supply–demand matching enabled by more accurate demand forecasting and inventory management. Heterogeneity tests indicate that the resilience-enhancing effects are more pronounced among non-state-owned firms, manufacturing enterprises, and firms in less technology-intensive industries. Overall, our findings provide empirical support for transaction cost economics, dynamic capability theory, and the resource-based view, highlighting the strategic role of digital investment in strengthening supply chain resilience in infrastructure-constrained settings and contributing to the aims of Sustainable Development Goal 9. Full article

The rise in Blockchain-based digital assets has transformed the financial ecosystems, which has also created complex governance and taxation challenges. The pseudonymous and cross-border nature of crypto transactions undermines traditional tax enforcement, leaving regulators such as the South African Revenue Service (SARS) reliant on voluntary disclosures with limited verification mechanisms, while existing Blockchain forensic tools and regulatory technologies (RegTechs) have advanced in anti-money laundering and institutional compliance, their integration into issues related to taxpayer compliance and locally adapted solutions remains underdeveloped. Therefore, this study conducts a state-of-the-art review of Blockchain forensics, RegTech innovations, and crypto tax frameworks to identify gaps in the crypto tax compliance space. Then, this study builds on these insights and proposes a conceptual model that integrates digital forensics, cost basis automation aligned with SARS rules, wallet interaction mapping, and non-fungible tokens (NFTs) as verifiable audit anchors. The contributions of this study are threefold: theoretically, which reconceptualise the adoption of Blockchain forensics as a proactive compliance mechanism; practically, it conceptualises a locally adapted proof-of-concept for diverse transaction types, including DeFi and NFTs; and lastly, innovatively, which introduces NFTs to enhance auditability, trust, and transparency in digital tax compliance. Full article

In the context of China’s dual-carbon agenda and the Digital China initiative, elucidating the role of digital literacy in shaping consumption-based household carbon emissions (HCE) is essential for advancing low-carbon urban living and supporting a broader green transition. Existing research has rarely examined, at the individual level, how digital capability shapes household consumption decisions and the structure of carbon emissions. Accordingly, this study draws on matched household-individual microdata from the China Family Panel Studies (CFPS). We employ a two-way fixed effects model, kernel density analysis, and qualitative comparative analysis. We test the nonlinear effect of digital literacy on household consumption-related carbon emissions and examine its heterogeneity. We also examined the mediating role of perceived environmental pressure, social trust and income level. The research results show that: (1) The net impact of digital literacy on carbon emissions related to household consumption shows an inverted U-shaped curve, rising first and then falling. When digital literacy is low, it mainly increases emissions by expanding consumption channels, reducing transaction costs and improving convenience. Once digital literacy exceeds a certain threshold, the mechanism will gradually turn to optimize the consumption structure, so as to support the low-carbon transformation of individuals. (2) The impact of digital literacy on HCE is structurally different in different types of consumption. In terms of transportation and communication expenditure, the emission reduction effect is the most significant, and with the improvement in digital literacy, this effect will become more and more obvious. For housing-related consumption, the turning point appeared the earliest. With the improvement in digital literacy, its effect will enter the emission reduction stage faster. (3) Digital literacy can reduce carbon emissions related to household consumption by enhancing residents’ perception of environmental pressure and strengthening social trust. However, it may also increase emissions by increasing residents’ incomes, because it will expand the scale of consumption, which will lead to an increase in carbon emissions related to household consumption. (4) The heterogeneity analysis shows that as digital literacy improves, carbon emissions increase more strongly among rural residents, people with low human capital, low-income households, and women. However, the turning-point threshold for emission reduction is relatively lower for women and rural residents. (5) Low-carbon transitions in household consumption are shaped by dynamic interactions among multiple factors, and multiple pathways can coexist. Digital literacy can work with environmental responsibility to endogenously promote low-carbon consumption behavior. It can also, under well-developed infrastructure, empower households and amplify the emission-reduction effects of technology. Full article

The financial services sector is experiencing unprecedented transformation through the adoption of virtualization technologies, encompassing cloud computing and edge computing digitalization initiatives that fundamentally alter operational paradigms and competitive dynamics within the industry. This systematic literature review employed a comprehensive methodology, analyzing peer-reviewed articles, systematic reviews, and industry reports published between 2016 and 2025 across three primary technological domains, utilizing thematic content analysis to synthesize findings and identify key implementation patterns, performance outcomes, and emerging challenges. The analysis reveals consistent evidence of positive long-term performance outcomes from virtualization technology adoption, including average transaction processing time reductions of 69% through edge computing implementations, substantial operational cost savings and efficiency improvements through cloud computing adoption, while simultaneously identifying critical challenges related to regulatory compliance, security management, and organizational transformation requirements. Virtualization technology offers transformative potential for financial services through improved operational efficiency, enhanced customer experience, and competitive advantage creation, though successful implementation requires sophisticated approaches to standardization, regulatory compliance, and change management, with future research needed to develop integrative frameworks addressing technology convergence and emerging applications in decentralized finance and digital currency systems. Full article

Integrating emerging Industry 4.0 technologies into smart factories has been widely discussed, particularly challenges regarding the practical use of a blockchain; one remaining challenge is the role of a blockchain beyond logistics and traceability, as well as its ability to support explicit trust measurement in real industrial environments. Existing studies often treat trust as a conceptual or cloud-oriented construction, without linking it to measurable production events. This study proposes a blockchain service-level agreement (SLA) to measure trust at an open-source frugal smart factory (SF). Trust is defined as a dynamic quantitative score derived from measurable process events, including estimated and response times, assembly correctness, and transaction outcomes; all of this is calculated through a smart contract implemented on a blockchain network. The approach is implemented in a tangram puzzle assembly process that integrates cyber-physical systems, edge computing, artificial intelligence, cloud computing, data analytics, cybersecurity, and the blockchain within a unified SF architecture. The framework was experimentally validated across four representative assembly scenarios: (i) the SF delivered the puzzle in time and was correctly assembled (${\lambda }_s$ = 0.1734), (ii) the puzzle was completed within tolerance time (${\lambda }_s$ = 0.0649), (iii) the puzzle was delivered on time and was incorrectly assembled (${\lambda }_s$ = 0.0005), and (iv) the puzzle was completed outside the tolerance time and was correctly assembled (${\lambda }_s$ = 4.91 × ${10}^{-5}$); demonstrating that the model accurately estimates expected assembly times and updates trust without manual intervention during a physical manufacturing task, addressing the limitations of prior conceptual and cloud-based approaches. The main research contributions include an operational SLA-based trust model, the demonstration of the feasibility of applying blockchain-based SLAs in a physical SF environment, and evidence that a blockchain can be justified as a mechanism for managing and measuring trust in SF, rather than solely for traceability or logistics. Full article

Blockchain technology has attracted considerable attention in the supply chain literature for its potential to enhance operational traceability, transparency, and trust, as well as to advance greening initiatives. Given current supply chain configurations, exploring barriers to implementation is a consequential agenda, and current studies have devoted substantial effort to identifying and offering guidance to address them. Despite recent findings, insights into how blockchain technology adoption can support green supply chain management are missing, particularly in the maritime sector, which receives limited attention. Thus, this work outlines a methodological approach to examine the suitability of maritime routes for addressing barriers to implementing blockchain technology in green supply chain management. Viewing the evaluation as a multi-criteria decision-making (MCDM) problem, the proposed approach performs the following actions on a case study evaluating four maritime lines. Firstly, from the 13 identified barriers in the literature review and expert interviews, nine relevant barriers were determined after one round of a Delphi process. These barriers eventually comprise the set of evaluation criteria. Secondly, to satisfy the assumption of criterion independence in most MCDM methods, this work proposes a novel extended grey relational analysis (GRA) that allows for the measurement of criterion independence based on the concept of grey relational space. Proposed here for the first time, the extended GRA offers a distribution-free overall independence index for each criterion based on pattern similarity. Finally, an integration of SWARA (Stepwise Weight Assessment Ratio Analysis) and ARAS (Additive Ratio Assessment) methods under Z-information is developed to address the evaluation problem involving expert judgments in a highly uncertain decision-making context. Results show that transaction-level uncertainty is the most critical barrier to blockchain adoption, followed by technology risks and higher sustainability costs. Among the four maritime lines, Line 3 is best prepared for a blockchain-enabled green supply chain. The agreement between these results and those of other MCDM methods is shown in the comparative analysis. Also, ranking remains unchanged even when the criteria weights are adjusted. The proposed approach provides a computationally efficient and tractable framework for maritime managers to make informed decisions about blockchain adoption to promote green supply chains. Full article

This study investigates the influence of access to mobile cashless technology on enterprise continuity intention and cash flow management skills. It also explores the influence of cashless technology, knowledge, and experience on enterprise continuity intention and cash flow management skills, and examines the direct relationship between cash flow management skills and enterprise continuity intention among micro and small enterprises during crises and in an unstable context. The 259 responses collected from micro and small entrepreneurs were analyzed by Partial Least Squares Structural Equation Modeling. The hypotheses tested reported a positive and significant relationship between access to mobile cashless technology and enterprise continuity intention and cash flow management skills. Furthermore, it was found that cashless technology knowledge and experience have a positive and significant relationship with enterprise continuity intention, as well as cash flow management skills. Finally, cash flow management skills were found to positively influence enterprise continuity intention. The study offers theoretical and practical implications for policymakers and other stakeholders to improve cashless transactions in the context of the study. Full article

The transition toward decentralized energy systems has amplified interest in peer-to-peer electricity trading. However, research on prosumer behavior in such markets remains fragmented, hindered by a lack of benchmarkable experimental infrastructure. Addressing this gap, the LabChain system was developed—a modular, interactive prototype designed to study human behavior in synthetic P2P electricity markets under controlled laboratory conditions. This system integrates real-world technologies, such as blockchain-based transaction backends, flexibility market interfaces, and asset control tools, allowing fine-grained observation of strategic and perceptual dimensions of prosumer activity. The research followed an iterative design approach to develop the infrastructure for experimental energy economics research, and to assess its effectiveness in aligning participant experience with design intentions. Based on the meta-requirements generality, affordance-centric design, and technological grounding, 13 detailed peer-to-peer market, software, and system requirements that allow for system evaluation were developed. As a proof of concept, seven participants simulated prosumer behavior over a week through interaction with the system. Their interaction with the system was analyzed through simulation data and focus group interviews, using a modified thematic content analysis with a hybrid inductive–deductive coding approach. The main achievements are (i) the design and implementation of the LabChain system as a modular infrastructure for P2P electricity market experiments, (ii) the development of an associated experimental workflow and research design, and (iii) its demonstration through an illustrative, proof-of-concept evaluation based on thematic content analysis of a single focus group session focusing on interaction and perceptions. The behavioral results from an initial session are limited, exploratory, and demonstrative in nature and should be interpreted as illustrative only. They nevertheless revealed tension between system flexibility and cognitive usability: while the system supports diverse strategies and market roles, limitations in interface clarity and information feedback constrain strategic engagement. Full article