ABSTRACT. Rural tourism destinations face the critical challenge of balancing economic development, environmental preservation, and cultural authenticity. While substantial research has exam-ined sustainable tourism practices, limited attention has been devoted to understanding how tourists perceive sustainability initiatives in rural contexts and how these perceptions trans-late into behavioural outcomes in rural contexts. This conceptual paper proposes an integra-tive multilevel framework that synthesises stimulus-organism-response theory, the theory of planned behaviour, place attachment theory, and social exchange theory to explain the for-mation and influence of tourist sustainability perceptions in rural destinations. The frame-work explicitly addresses the roles of destination-level factors, tourist-level moderators, and psychological mediators in shaping both private and public pro-environmental behaviours. Eight research propositions are developed to guide future empirical investigations. This framework contributes to the tourism literature by providing a holistic understanding of the psychological mechanisms linking rural tourism experiences with sustainable behavioural outcomes, while offering practical implications for destination managers, policymakers, and local communities seeking to enhance destination competitiveness through authentic and sustainable practices.

ABSTRACT. This study investigates the intrinsic determinants influencing entrepreneurial intentions (EI) among Vietnamese university students (SV), a population recognized as future drivers of innovation and economic growth. Grounded in the Theory of Planned Behavior and complementary frameworks, the research explores how creativity, entrepreneurial skills, entrepreneurial attitudes, opportunity recognition, psychological capital, and perceived value of entrepreneurship shape EI. A mixed-methods design was employed, beginning with qualitative expert discussions to refine measurement scales, followed by a preliminary quantitative survey. Data were collected from 200 students in Ho Chi Minh City, Dong Nai, and Can Tho, yielding 116 valid responses for analysis. Using SmartPLS 4.0, we conducted reliability tests, exploratory factor analyses, and measurement model evaluations. Results confirm that all constructs meet reliability and validity thresholds, with composite reliability (CR) values exceeding 0.80 and average variance extracted (AVE) values surpassing 0.60. Discriminant validity was supported through HTMT ratios below 0.85. These findings demonstrate that the adapted scales for intrinsic determinants are robust in the Vietnamese context and provide a solid foundation for subsequent large-scale testing of the proposed structural model. Preliminary evidence highlights the significant role of creativity, entrepreneurial skills, and psychological capital in enhancing perceived value and EI, while attitudes and opportunity recognition further strengthen entrepreneurial drive. The study concludes that focusing on intrinsic factors within higher education can substantially foster entrepreneurial mindsets, thereby equipping students to pursue entrepreneurship as a viable career path. These preliminary results contribute both theoretically by validating multidimensional constructs of intrinsic determinants, and practically by offering higher education institutions actionable insights to design policies and programs that nurture student entrepreneurship

ABSTRACT. This study investigates the impact of Construction 4.0 (C4.0) technologies on the sustainable performance of Vietnamese construction firms, with particular emphasis on the post-adoption phase. In developing nations such as Vietnam, rapid urbanisation is placing mounting pressures on the construction industry, making the pursuit of sustainable development goals especially challenging. To address this issue, a survey of 215 Vietnamese executives was conducted, using 34 measurement items grounded in diffusion of innovation (DOI) theory and socio-technological systems (STS) theory. Structural equation modelling (SEM) reveals that five key attributes of C4.0 adoption—relative advantage, compatibility, trialability, observability, and complexity—positively influence sustainable performance across economic, social, and environmental dimensions. In addition, compatibility, trialability, observability, and complexity are found to significantly mediate the relationship between relative advantage and sustainability outcomes. The findings contribute to the limited body of firm-level empirical evidence on the post-adoption of C4.0 technologies in developing countries, offering new insights into the technological and social dimensions of innovation diffusion. The results demonstrate how C4.0 technologies can help construction firms refine workflows, reduce resource waste, optimise energy consumption, and promote socially and environmentally responsible practices. By aligning digital innovation with the triple bottom line, the study highlights the potential of C4.0 adoption to enhance sustainable performance and provide a pathway for overcoming the pressing development challenges faced by the construction sector in emerging economies.

ABSTRACT. This study looks at how to use Artificial Intelligence (AI) with Blockchain technology to make supply chains more open, efficient, and ethical, as more people want businesses to be more environmentally friendly. The study reveals that AI can help with data management, predictive analytics, and decision-making, which can improve environmental, social, and governance (ESG) performance. It also talks about how Blockchain's unchangeable record may ensure that products can be traced back to their source and are ethically generated, which increases trust in consumers. The study looks at data sets from cocoa, coffee, and cotton and uses AI-driven models like Random Forest Regressors to develop predictions about how sustainable they will be and how different parts of the supply chain will affect them. The study reveals how Blockchain technology may help protect the ability to trace products and stop fraud, which makes ethical sourcing stronger. The study shows how AI and Blockchain may work together to make businesses more open and responsible in their supply chains and help the environment. The results suggest that this combined strategy can influence how businesses perform, making them more responsible and helping to make the future a better place for the environment.

ABSTRACT. This study examines the causal relationship between Environmental, Social, and Governance (ESG) practices, corporate valuation, and firm performance. Utilizing stock market data from 44 countries spanning 2016 to 2022, the analysis applies difference-in-differences (DID) estimation, Granger causality tests, and clustering techniques to classify and evaluate ESG and financial characteristics across firms. The findings reveal that companies with higher ESG scores exhibit significantly lower stock price volatility and experience positive abnormal returns, suggesting that investors increasingly integrate ESG considerations into their valuation and investment decisions.

ABSTRACT. Commercial banks play a vital role in the economic system. Their objective is to maximize profitability, which is influenced by a variety of factors. Therefore, it is essential to manage controllable or internal factors effectively. This study focuses on commercial banks in Laos, where such factors may differ from those in Thailand. The aim of this research is to examine the relationship between internal financial factors and the profitability of banks in Laos. Data were collected from 27 commercial banks that publicly disclosed their annual financial statements over a five-year period from 2019 to 2023. The study employs summary statistics and multiple regression analysis. The independent variables include loan growth rate, deposit growth rate, total asset growth rate, bank age, bank size, and loan-to-deposit ratio, while the control variables are Laos’ GDP growth rate and the US dollar/kip exchange rate. The findings indicate that the total asset growth rate has a significant positive effect on Net Profit Margin (NPM) and Return on Assets (ROA), while bank size and the loan-to-deposit ratio have significant negative effects on both NPM and ROA. However, none of the examined variables had a statistically significant effect on Return on Equity (ROE).

ABSTRACT. Access to finance remains a persistent challenge for small and medium-sized enterprises (SMEs) in developing countries, as traditional credit scoring systems rely heavily on collateral and formal financial histories that many SMEs lack. This study aims to examine how artificial intelligence (AI)-driven credit scoring influences financial inclusion by enabling alternative assessments of creditworthiness. A qualitative, multiple-case research design is employed, comparing two anonymized cases, a digital bank embedded in a mature platform ecosystem, and a fintech firm operating across diverse developing economies. The findings show that AI reduces information asymmetries by drawing on alternative data, thereby broadening access to collateral-free loans for previously excluded SMEs. Theoretically, the study extends information asymmetry theory by illustrating how digital footprints become new informational assets, advances institutional theory by showing how regulatory conditions mediate AI outcomes, and enriches the resource-based view by framing AI and data capabilities as dynamic competitive resources. Practically, the research provides guidance for financial institutions, fintech firms, and policymakers to design AI-enabled credit systems that enhance inclusion while ensuring transparency, fairness, and consumer protection.

ABSTRACT. The global agenda toward sustainable development encourages Islamic countries to balance economic growth with environmental preservation. This study aims to analyze the impact of Islamic finance, renewable energy, technological innovation, and environmental policies on green economic growth in selected member countries of the Organization of Islamic Cooperation (OIC), both in the short and long term, while considering the moderating role of institutional quality. The study employs panel data from the 2019–2023 period and utilizes the Generalized Method of Moments (GMM) to address potential endogeneity and bias in the dynamic model. Control variables include inflation, foreign direct investment (FDI), gross domestic product (GDP), and population to ensure more robust results. Empirical findings indicate that Islamic finance, renewable energy, and technological innovation have a positive and significant effect on green economic growth, while the impact of environmental policies depends on the level of institutional quality. Furthermore, institutional quality is found to have a positive moderating effect, strengthening the relationship between Islamic finance and green economic growth. These findings underscore the importance of strong institutions in enhancing the effectiveness of financial and green technology policies. This study contributes to the development of the sustainable Islamic finance literature by offering policy implications for OIC countries to strengthen institutional frameworks, promote renewable energy innovation, and integrate financial instruments in achieving sustainable development goals.

ABSTRACT. Effective loan-default prediction is central to strengthening credit-risk management and reducing financial exposure in lending institutions. This study evaluates five supervised learning models—Logistic Regression, Random Forest, Gradient Boosting, LightGBM and an Artificial Neural Network—applied to a large, imbalanced loan-default dataset. A consistent preprocessing pipeline and best threshold optimisation framework were employed to enhance comparability and improve minority-class detection. The results show that boosting-based ensemble methods, particularly LightGBM, deliver the highest predictive performance, revealing complex interactions among borrower and loan attributes while maintaining operational efficiency. Key financial variables such as upfront charges and property value emerge as strong predictors across models, underscoring their importance in risk assessment. The findings highlight the value of combining robust preprocessing, imbalance handling and modern ensemble algorithms to support more reliable and actionable credit-risk decision-making. These insights offer practical implications for institutions seeking scalable, data-driven approaches to credit evaluation and provide a methodological foundation for future risk-modelling research.

ABSTRACT. Social media influencers play a huge role when it comes to a business’ marketing and PR strategy. However, because of the large variety and volume of influencers and posts, it can be difficult for businesses to identify content themes and audience interest in social media posts, making it difficult for them to position their brand campaigns better in the industry. Clustering, which is a data science approach used to reveal distinct groupings on data based on similar internal group characteristics, can identify dominant themes within social media posts. This paper proposed a cross-modal approach for the clustering of multimodal influencers posts by incorporating HEALNet for multimodal fusion, HDBSCAN for clustering and topic modelling using BERTopic’s c-TF-IDF for getting the topic labels of clusters. Topic coherence was evaluated using the Neighborhood Purity (NP) score, which measures local topic consistency among nearest neighbors; the Intra-Topic Embedding Coherence (ITEC), which quantifies the similarity of embeddings within each topic; the Intra-Topic Similarity (IntraTS), which captures the average pairwise similarity of samples within topics; the Inter-Topic Similarity (InterTS), which measures the similarity between topic centroids; and Cv topic coherence, which assesses the semantic interpretability of topics based on the co-occurrence of their most representative words in the corpus. The results showed that the clusters generated were locally coherent and well separated, with 0.9111 and 0.8522 mean NP scores for 15 and 30 nearest neighbors respectively, 0.8976 mean ITEC, 0.8072 mean IntraTS, -0.0271 mean InterTS, and 0.5188 Cv topic coherence. These findings demonstrate that the fusion model effectively captured cross-modal relationships and produced meaningful and internally consistent groups of topics, with strong intra-cluster cohesion, minimal inter-cluster overlap, and overall acceptable level of topic coherence.

ABSTRACT. Algorithmic trading has transformed financial markets by automating execution and optimising portfolio management, but the integration of artificial intelligence (AI) has extended this transformation by enabling autonomous strategy generation, dynamic stress testing, and personalised wealth management. These advances raise critical questions about how AI may reshape not only institutional operations but also the stability of global financial systems. The aim of this study is to examine how AI-driven algorithmic trading and portfolio management influence financial stability, with particular attention to the dual role of enhancing efficiency and introducing systemic risks. A qualitative multiple-case research design is employed, focusing on three cases. The findings show that AI extends analytical capacity by modelling complex correlations and simulating stress scenarios, enhances adaptability in volatile markets, and broadens access to sophisticated portfolio tools for retail investors. However, the results also reveal systemic risks, including strategy convergence, herding behaviour, and challenges of transparency and regulatory oversight. The research offers guidance for asset managers, investment banks, fintech firms, and regulators on balancing innovation with oversight to ensure that AI strengthens rather than destabilises global financial stability.

ABSTRACT. Corporate sustainability initiatives have come under growing fire for greenwashing, which erodes the credibility of sustainability marketing and erodes long-term consumer trust. Due to their great purchasing power and value orientations, Generation Z is especially vulnerable to deceptive claims and is a prime target for advertisers. However, the majority of the research that has already been done is survey-based and has a narrow scope. As a result, it offers little insight into how Generation Z in developing markets like Vietnam truly perceives and reacts to these kinds of ads. By investigating how Generation Z consumers distinguish between signs of authenticity and greenwashing and how these assessments influence brand loyalty, trust, and purchase intentions, this study fills that vacuum more generally. The study advances theoretical understanding and offers useful advice for companies and politicians looking to refute false sustainability claims by examining the subtle cues that support or undermine green views through qualitative methodologies. This study employs a qualitative design utilizing semi-structured interviews with 20–30 Gen Z participants (aged 18-27). Participants are selected through purposive sampling with maximum variation to ensure the diversity of demographic background and environmental engagement levels. The collected data are analyzed using a hybrid inductive–deductive thematic coding approach, providing in-depth insights into consumers' perceptions and attitudes toward sustainability messaging and the greenwashing phenomenon. This research substantiates its critical relevance by contributing to expanding theoretical frameworks and literature regarding trust formation under sustainability claims. Furthermore, considering practical and managerial aspects, it proposes implementable directives for businesses, marketers and managers to design reliable and verifiable cue-based green marketing campaigns while combating greenwashing accusations by obstructing vague eco-claims in sustainability communications. On the policy front, specific guidance about anti-greenwashing regulations suggests corporate communications’ transparency and regular compliance checks to prevent deceptive practices, thereby supporting customers’ protection and encouraging the shift from greenwashing to green trust.

ABSTRACT. The Food and Agriculture Organization of the United Nations (FAO) has mentioned that Climate-Smart Agriculture (CSA) aims to three problems: ensuring food security, improving adaptation capacity, and reducing green-house gas emissions. This approach has become essential for promoting sustainable agricultural development worldwide. However, studies that synthesize and summarize research results on CSA remain limited, especial-ly in Southeast Asia. This work aims to provide a systematic overview of international research on CSA. Studies on CSA during the period 2010–2025 aim to: (1) identify trends in the development of CSA and the scope of its practical applications worldwide; (2) identify the main analytical frameworks and methodological approaches used; (3) draw empirical les-sons and formulate policy implications suitable for the Mekong Delta (ĐBSCL) in Vietnam. In accordance with PRISMA guidelines, we selected the literature from the Web of Science database. The results show that the majority of studies on CSA are concentrated in African and South Asian countries, while studies on Southeast Asian countries remain relatively limited. The main trends in the use of "smart" technologies in agricultural production, the analysis of factors affecting the adoption of CSA practices, and a noticeable increase in the use of quantitative methods and models. In order to develop CSA in the Mekong Delta, it is necessary to focus on strat-egies to adapt to each geographical region flexibly, focus on the application of mechanization and technology in production, and implement preferential policies on credit and risk insurance in the industry. At the same time, it is necessary to add a multidimensional approach to CSA research in this region.

ABSTRACT. This research proposes a scalable, data-driven framework to assess the credibility of corporate ESG communication by comparing firms’ sustainability narratives with independent third-party ESG ratings. Focusing on S&P 500 companies, the study applies Natural Language Processing (NLP)—specifically FinBERT, a transformer model tailored to financial and ESG-related discourse—to extract sentiment and thematic structures from the Risk Factors and Management’s Discussion and Analysis sections of 10-K filings.

The derived textual metrics are then aligned with external ESG performance scores from established rating agencies using entity normalization and fuzzy-matching algorithms to ensure consistency across reporting years. This allows the construction of a Greenwashing Discrepancy Score (GDS) that measures the degree of divergence between optimistic corporate ESG narratives and independent evaluations. Higher scores indicate potential greenwashing—where language signals exceed objectively assessed sustainability performance.

Cross-sectional and longitudinal analyses explore whether firms exhibiting high GDS values demonstrate lower ESG consistency or weaker governance indicators, thus challenging the reliability of their sustainability claims. The results provide an empirical basis for detecting misalignment between corporate self-representation and third-party assessments.

This work contributes to the Green Finance and ESG integration discourse by introducing a transparent, reproducible methodology for cross-validating corporate sustainability narratives. It demonstrates how advanced NLP and quantitative ESG data can be combined to strengthen accountability, improve investor confidence, and inform regulatory oversight in sustainable finance.

ABSTRACT. This paper proposes a comprehensive research framework to examine the dynamic relationship between time management and employee performance within the evolving landscape of artificial intelligence (AI) integration in the workplace. Although extensive literature exists on traditional time management strategies and performance management systems, there remains a critical research gap in understanding how AI adoption is reshaping time management practices and influencing individual performance outcomes. The proposed framework builds on the Ability-Motivation-Opportunity (AMO) theory and classic time management models, while incorporating the disruptive and enabling roles of AI technologies. To develop this conceptual model, the study employs a qualitative methodology, including systematic literature review and expert interviews, to synthesize existing knowledge and identify emerging patterns. The framework also outlines key moderating variables, such as employee digital readiness and organizational support, that may condition the impact of AI on time-use efficiency and work outcomes. This research contributes to theoretical advancements by bridging time management literature with AI-driven organizational behavior studies. It also offers practical implications for managers and HR practitioners seeking to harness AI technologies to enhance performance sustainably.

ABSTRACT. This paper examines gamification in human resource management (HRM) through the lens of marketing and consumer behavior, framing employees as internal consumers. It integrates key motivational theories—Self-Determination, Flow, and Expectancy—with consumer behavior principles to explain how game elements enhance recruitment, training, engagement, and retention. While gamification can boost motivation, loyalty, and performance, it also raises ethical and cultural challenges, including data privacy and overreliance on extrinsic rewards. Methodological insights highlight the value of mixed methods, longitudinal designs, and cross-cultural research to capture sustained impacts. Findings suggest that, when ethically implemented, gamification offers a strategic HRM tool for fostering sustainable engagement and organizational loyalty.

ABSTRACT. Over the past century, Financial Technology (FinTech) has undergone significant development, bringing about transformative changes in the financial services and markets. The application of FinTech to create value centered around customers is of particular relevance to small and medium-sized enterprises (SMEs) in emerging economies. Although global research on the digital transformation and technology adoption of SMEs is expanding, studies focusing on developing countries, especially Vietnam, remain relatively scarce. This study focuses on four key technologies—Blockchain, the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data Analytics (BDA)—which have a profound potential to revolutionize retail SMEs in Vietnam. By examining existing literature and supporting theories such as Technology-Organization-Environment (TOE), Diffusion of Innovations (DOI), Resource-Based View (RBV), and Dynamic Capabilities View (DCV), this research presents a conceptual framework that explores how FinTech adoption influences the performance of Vietnamese retail SMEs. The non-financial performance metrics explored include customer satisfaction, employee productivity, process efficiency. The findings of this study aim to establish a foundation for further empirical research, contributing to the existing literature and offering valuable insights to SME owners and managers on which FinTech solutions provide the most value under varying conditions to facilitate better investment and operational decisions

ABSTRACT. This paper introduces a blockchain-based framework for automated financial settlement that enhances transparency and auditability. The system combines identity management through KYC-gated participation with escrow mechanisms and standardized event logging. Implemented on Ethereum-compatible platforms with support for permissioned deployments, our solution demonstrates competitive performance in throughput, latency, and cost efficiency. Comparative analysis against traditional payment rails reveals that blockchain settlement can achieve speeds comparable to instant payment systems while providing superior audit capabilities through immutable transaction trails. The framework addresses key operational considerations, including compliance integration, privacy preservation, and governance controls, establishing a foundation for programmable settlement with applications across both public and private blockchain environments.

ABSTRACT. This review aims to explore latent themes in existing publications related to human-centricity, digital transformation, sustainability, and risk management within the logistics and supply chain industry. The analysis is based on a dataset of 133 full-text articles sourced from two well-known scholarly databases: Scopus and Web of Science (WoS). By utilizing Latent Dirichlet Allocation (LDA) topic modeling , this study identifies emerging research trends and gaps. Based on these insights, the review also recommends potential future research direction to advance the integration of these aspects, addressing both opportunities and challenges for the development of more resilient, sustainable, and human-centered logistics systems.

ABSTRACT. In the era of sustainable globalization, free trade agreements (FTAs) have increasingly incorporated social and labour dimensions, reflecting a shift from market liberalization toward responsible and inclusive trade governance. Among these, the elimination of child labour has evolved from a moral aspiration into a legally recognized international norm. This paper examines the evolution of international standards on child labour within new-generation FTAs, focusing on how commitments have transformed from soft law provisions to enforceable obligations. Adopting a normative and comparative analytical approach, the study traces the development of child labour clauses across major FTAs, including the EU–Vietnam Free Trade Agreement (EVFTA), the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP), the United States–Mexico–Canada Agreement (USMCA), and the EU–Chile Agreement. The analysis is grounded in the theoretical frameworks of “norm diffusion” and “legalization,” highlighting the progressive strengthening of obligation, precision, and enforcement mechanisms. Findings indicate a clear trend toward the legalization and institutionalization of labour norms within trade law, where cooperation-based models such as the EVFTA coexist with sanction-based approaches exemplified by the USMCA. The paper argues that this normative evolution represents a critical step in aligning global trade governance with Sustainable Development Goal 8.7 on the elimination of child labour. The study also offers conceptual implications for developing countries, including Vietnam, in harmonizing trade policy with international labour standards.

ABSTRACT. This study explores the critical success factors influencing the implementation of smart food safety management systems (SFSMS) and its interactive impact on firm economic performance. Built on Resource-Based View and Stakeholder Theory, this study highlights how internal and external factors influence SFSMS implementation and business success. This research especially compares the significance of critical success factors between BRC certified and non-certified food companies to pave a detailed picture for effective operation. After collecting data from 243 Asian food exporting companies, SEM analysis was used for hypothesis testing and Fuzzy-Delphi was then used to weight the importance of success factors. The SEM results show that critical success factors significantly influence SFSMS implementation and firm performance. However, while SFSMS implementation is beneficial, it does not influence economic performance. Particularly, BRC certified companies demonstrated a systematic and policy-based approach while non-BRC certified companies demonstrated a flexible and pragmatic approach in finance, technology, and external support. Both companies emphasized support from NGOs. These findings underscore the need for internal capacity building and external collaboration to improve food safety practices and competitiveness. We encourage policymakers and business associations to support businesses, especially non-certified companies, through training, increased funding, and collaboration with NGOs to capture the long-term benefits of SFSMS.

ABSTRACT. This study explores Supply Chain Quality Management 4.0 (SCQM4.0), an emerging strategic approach that integrates quality management and supply chain practices with Industry 4.0 technologies to promote sustainable manufacturing. Focusing on Vietnam's garment industry, the research examines key factors influencing SCQM4.0 adoption and its impact on sustainable supply chain performance. Using data from 221 garment firms and applying Structural Equation Modelling (SEM), the study builds on the Technology-Organisation-Environment (TOE) framework to identify six key determinants. These are grouped into organisational practices (Top Management 4.0 Commitment, Workforce 4.0 Development, I4.0 Adoption Preparedness) and techno-environmental practices (I4.0-Driven Supply Chain Integration, I4.0-Enabled Information Sharing, Digital Infrastructure 4.0 Readiness). The findings confirm strong positive relationships between these practices and the environmental, social, and economic performance of supply chains. The study offers valuable insights for supply chain professionals, researchers, and policymakers aiming to enhance resilience and sustainability through SCQM4.0.

ABSTRACT. Sustainable development (SD) has crystallized as a foundational global legal principle aimed at reconciling economic growth with social progress and environmental protection. This concept has evolved through the historical development of international law, as reflected in key instruments such as the World Conservation Strategy (1980), the Rio Declaration(1992), the Johannesburg Plan of Implementation (2002), and the 2015 2030 Agenda for Sustainable Development with its 17 Sustainable Development Goals (SDGs). In Vietnam, SD has become an urgent imperative amid rising environmental pollution, looming resource depletion, and emerging social inequalities driven by rapid, uncontrolled economic growth. Methodologically, this study employs doctrinal analysis of key legal texts – including the 2013 Constitution, the 2020 Law on Environmental Protection, and the National Strategy for Sustainable Development – and uses a comparative approach to juxtapose the European Union’s and Vietnam’s institutional frameworks for SD. The comparison focuses on (i) legal mechanisms to integrate the economic, social, and environmental pillars of sustainability, (ii) the effectiveness of enforcement and oversight mechanisms. The findings identify three principal legal challenges. First is the tension between pro-growth policies and environmental protection rules, which often leads to policy conflicts. Second are the legal and regulatory gaps in controlling industrial pollution and managing the energy transition. Third is the limited enforcement capacity of local authorities, compounded by the absence of independent monitoring mechanisms to ensure compliance with environmental laws.

ABSTRACT. Key transformations in business, the economy, and public policy are increasingly shaped by the dual forces of sustainability and digital innovation. These concur-rent transitions are redefining modern business models, with the circular econo-my emerging as a focal point in the digital age—bringing both opportunities and challenges for enterprises and policymakers. Digital innovation acts as a catalyst for sustainable development, as revealed through cross-disciplinary analysis. The restructuring of global value chains under the pressures of ESG standards and digital technologies, combined with the evolving landscape of economic policy in the context of climate change, calls for an integrated analytical framework.

ABSTRACT. This study adopts relational embeddedness theory through the lens of cognitive distance to conceptualize a pattern of partner selection among venture capital firms in blockchain entrepreneurial finance. Using the case-control study approach on a unique longitudinal dataset comprising 8,342 funding rounds across 5,248 blockchain startups as of August 2024, we identify an inverted U-shaped relationship between the depth of a VC pair’s ties and their likelihood of co-investing. This transactional pattern is moderated by the cognitive distance between VC partners, measured through their investment tendencies towards tokenization and follow-on support. We find that relational embeddedness and cognitive similarity function as substitutes when the partnership is newly established. AS the relationship evolves, however, VCs become increasingly attracted by resources from cognitively distant partners. Nevertheless, we argue that VC partnerships tend to be more stable and longer-lasting when characterized by greater cognitive similarity. Additionally, we show that the founding team’s governance power over the venture amplifies the need for trust among funders. In the context of tokenized startups, lead VCs are more likely to collaborate with partners with whom they share greater prior co-investment experience when a larger share of tokens is retained by the venture.

ABSTRACT. As sustainability continues to influence people’s propensity to travel and the competitive landscape of the hospitality industry, serviced apartments are gaining attention as an emerging alternative to traditional hotels — offering a balance of economic efficiency, social engagement, and environmental re-sponsibility. Yet, little area of research interests addresses why travelers choose these lodging options, especially in emerging destinations where longer stays are becoming more common. This study applies the Theory of Planned Behavior (TPB) to explore how perceptions of sustainability practic-es in serviced apartments influence travelers’ attitudes, subjective norms, perceived behavioral control, and ultimately their intention and actual deci-sion to stay in serviced apartments during extended visits to Nha Trang, Vi-etnam. The authors propose a framework that links perceptions of green practices — such as energy-saving systems, local community partnerships, and value-for-money benefits — to the psychological drivers of visitors’ be-havior. Based on data collected from 312 travelers, regression results show that these sustainable efforts conducted via environmental, social, and eco-nomic practices significantly improve attitudes, strengthen social influence, and boost perceived control, all of which increase intention and booking be-havior towards serviced apartments. By demonstrating how sustainability can become a decisive factor in accommodation choice, the study offers practical insights for lodging operators aiming to position serviced apart-ments as credible green alternatives to hotels. It also extends the application of TPB in sustainable tourism by showing how travelers’ values translate in-to concrete decisions in a real-world context.

ABSTRACT. This study investigates the asymmetric impact of Renewable Energy Use (RENEW), Bank Domestic Credit (BDS), and Industrial Robots (ROBOTS) on environmental sustainability, measured by the Ecological Footprint (EF) and Greenhouse Gas Emissions (GHG), in the five leading robot-installing countries (China, Japan, the United States, Germany, and South Korea) from 2011 to 2023. Given the confirmed cross-sectional dependence and slope heterogeneity, advanced panel data techniques were employed, including Panel Cointegration and the Moment Quantile Regression (MMQR) model as the primary estimation technique, supplemented by FMOLS and Robust Least Squares for robustness checks. The MMQR results reveal significant heterogeneity across environmental quantiles. GDP per capita (GDPPC) and BDS exhibit a robust, uniformly negative impact on both EF and GHG across the distribution, confirming the pro-environmental role of financial development and supporting the Environmental Kuznets Curve (EKC) hypothesis. Crucially, the coefficients for RENEW are consistently negative and stronger at the upper quantiles of EF and GHG, indicating that renewable energy is most effective in mitigating environmental degradation in the most polluted economies. Conversely, ROBOTS present a uniformly positive and significant relationship with both EF and GHG, suggesting that the current wave of industrial automation primarily drives environmental pressure. Panel Granger Causality tests confirm significant bidirectional and unidirectional relationships among the variables. These findings urge policymakers in major industrial economies to enforce strict "green" standards for automation and strategically leverage financial credit to accelerate the renewable energy transition.

ABSTRACT. This study explores how Evolutionary Machine Learning (EML), an adaptive optimisation approach within artificial intelligence, can drive the transition toward net-zero construction and sustainable business performance. Drawing on Ecological Modernisation Theory, Adaptive Structuration Theory, and the Diffusion of Innovation framework, the research develops and empirically tests a strategic model explaining how EML-enabled technologies enhance both carbon neutrality and organisational outcomes. Using survey data from 213 Vietnamese construction firms, the findings reveal that the success of EML adoption depends on aligning technological integration with operational realities, stakeholder readiness, and long-term innovation strategies. EML is shown to optimise resource allocation, project scheduling, and carbon footprint management, providing firms with competitive and environmental advantages. The study contributes to sustainable digital transformation discourse by positioning EML as a practical tool for solving complex optimisation challenges in developing economies, offering actionable insights for policymakers, practitioners, and researchers seeking to leverage intelligent systems for climate-resilient and high-performing construction supply chains.

ABSTRACT. This study evaluates the current status of supply chain linkage in agricultural cooperatives in An Giang province and analyzes the potential application of the Online-to-Offline business model to enhance the effectiveness of supply chain linkages. Based on surveys and data collection from cooperatives, farmers, distribution enterprises, and supporting service providers, the research analyzes the levels of vertical and horizontal linkages within the local agricultural supply chain. Quantitative analysis methods are employed to assess the impacts of key factors in the Online-to-Offline model, including traceability, logistics efficiency, and customer interaction, on supply chain linkage performance. The findings reveal that several cooperatives have begun adopting digital platforms for order management, online payment, and product traceability, thereby improving transparency and market connectivity. The study confirms the potential for scaling up the Online-to-Offline model in the agricultural sector and proposes policy recommendations to support cooperatives in digital transformation, strengthen supply chain linkages, and build a sustainable digital ecosystem in Viet Nam.

ABSTRACT. Green bonds serve a crucial purpose of funding environmental initiatives, but their performance is difficult to predict since the interaction between financial, environmental, and policy-related factors is rather complex. This paper proposes to improve on the forecasting of green bond performance using machine learning (ML), i.e., a new ensemble stacking model called GreenStack. The suggested architecture incorporates Random Forest and LightGBM base learners with Ridge Regression, which will act as a meta-learner and catch nonlinear relations and minimize overfitting. Trained on a dataset of 1601 green bonds endowed with (i) ESG scores, (ii) credit ratings, and (iii) macroeconomic indicators, GreenStack exploits a robust pre-processing of samples, access to time-series aware train-test splitting, along with interpretable feature selection. GreenStack markedly exceeded the baseline models, receiving an R2 of 0.9766 and the minimum values of MAE and RMSE (0.4408 and 0.5670, respectively), which indicates high rates of prediction accuracy and generalization capacity. The model also gives actionable ideas by presenting the main drivers that include market demand, credit rating, and government support. Conclusively, GreenStack can provide an interpretable and viable ML model that predicts how green bonds fare and becomes a contribution to the emerging field of green finance analytics.