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Heightened interest in providing transparency and security within business systems and processes has triggered a multitude of studies on the adoption of emergent technologies. Blockchain technology (BCT) has emerged to address issues of transparency, efficiency, and security in manufacturing, supply chain, enterprise, and e-commerce systems. Given the enormous success of BCT in financial applications, there is keenness to explore other application areas. This research explores the integration of BCT in enterprise systems to enhance the engineering change management (ECM) process. Employing the technology acceptance model (TAM) to assess adoption intentions from the perspective of users, two concepts were examined: perceived usefulness (PU) and perceived ease of use (PEU). A quantitative survey method collected and analysed the perception data. The findings revealed a positive perception of both usefulness and ease of use from the users in adopting a blockchain-enabled enterprise platform to support the ECM process. More detailed findings uncovered the workflow management function as an area that may require further technical exploration. These findings provide valuable insights on the benefits of blockchain in optimizing business processes and contribute to the growing literature about perceptions of integrating BCT in enterprise platforms. Full article

Due to uncertain business climate, fierce competition, environmental challenges, regulatory requirements, and the need for responsible business operations, organizations are forced to implement sustainable supply chains. This necessitates the use of proper data analytics methods and tools to monitor economic, environmental, and social performance, as well as to manage and optimize supply chain operations. This paper discusses issues, challenges, and the state of the art approaches in supply chain analytics and gives a systematic literature review of big data developments associated with supply chain management (SCM). Even though big data technologies promise many benefits and advantages, the prospective applications of big data technologies in sustainable SCM are still not achieved to a full extent. This necessitates work on several segments like research, the design of new models, architectures, services, and tools for big data analytics. The goal of the paper is to introduce a methodology covering the whole Business Intelligence (BI) lifecycle and a unified model for advanced supply chain big data analytics (BDA). The model is multi-layered, cloud-based, and adaptive in terms of specific big data scenarios. It comprises business process modeling, data ingestion, storage, processing, machine learning, and end-user intelligence and visualization. It enables the creation of next-generation BDA systems that improve supply chain performance and enable sustainable SCM. The proposed supply chain BDA methodology and the model have been successfully applied in practice for the purpose of supplier quality management. The solution based on the real-world dataset and the illustrative supply chain case are presented and discussed. The results demonstrate the effectiveness and applicability of the big data model for intelligent and insight-driven decision making and sustainable supply chain management. Full article

With the increasing demand for accurate multimodal data analysis in complex scenarios, existing models often struggle to effectively capture and fuse information across diverse modalities, especially when data include varying scales and levels of detail. To address these challenges, this study presents an enhanced Swin Transformer V2-based model designed for robust multimodal data processing. The method analyzes urban economic activities and spatial layout using satellite and street view images, with applications in traffic flow and business activity intensity, highlighting its practical significance. The model incorporates a multi-scale feature extraction module into the window attention mechanism, combining local and global window attention with adaptive pooling to achieve comprehensive multi-scale feature fusion and representation. This approach enables the model to effectively capture information at different scales, enhancing its expressiveness in complex scenes. Additionally, a cross-attention-based multimodal feature fusion mechanism integrates spatial structure information from scene graphs with Swin Transformer’s image classification outputs. By calculating similarities and correlations between scene graph embeddings and image classifications, this mechanism dynamically adjusts each modality’s contribution to the fused representation, leveraging complementary information for a more coherent multimodal understanding. Compared with the baseline method, the proposed bimodal model performs superiorly and the accuracy is improved by 3%, reaching 91.5%, which proves its effectiveness in processing and fusing multimodal information. These results highlight the advantages of combining multi-scale feature extraction and cross-modal alignment to improve performance on complex multimodal tasks. Full article

Urban non-point source (NPS) pollution is an important risk factor that leads to the deterioration of urban water quality, affects human health, and destroys the ecological balance of the water environment. Reasonable risk prevention and control of urban NPS pollution are conducive to reducing the cost of pollution management. Therefore, based on the theory of “source–sink” in landscape ecology, combined with the minimum cumulative resistance (MCR) model, this study considered the influence of geographic-environment factors in Shenyang’s built-up area on pollutants in the process of entering the water body under the action of surface runoff, and evaluated its risk. The results indicated that the highest pollution loads are generated by road surfaces. High-density residential zones and industrial zones are the main sources of urban NPS pollution. Impervious surface ratios and patch density were the dominant environmental factors affecting pollutant transport, with contributions of 56% and 40%, respectively. The minimum cumulative resistance to urban NPS pollution transport is significantly and positively correlated with the distance from water bodies and roads. Higher risk areas are mainly concentrated in the center of built-up areas and roads near the Hun River. Green spaces, business zones, public service zones, development zones, and educational zones demonstrate the highest average risk index values, exceeding 29. In contrast, preservation zones showed the lowest risk index (7.3). Compared with the traditional risk index method, the method proposed in this study could accurately estimate the risk of urban NPS pollution and provide a new reference for risk assessments of urban NPS pollution. Full article

In today’s fast-paced business settings, the metaverse as a shared marketplace has gained popularity and is helping businesses to develop crucial business strategies in their pursuit of sustainable performance. However, a lack of understanding and knowledge about the effectiveness of the metaverse and its related technologies creates a barrier. Therefore, the current study fills this gap and uses organizational information-processing theory to develop the theoretical framework to examine metaverse-related technologies (artificial intelligence and blockchain technology—BCT) and their direct and indirect effects on sustainable business performance, which no other study has examined. Using purposive sampling, the sample data from 326 SMEs were gathered and analyzed using a partial least square structural equation modeling (PLS-SEM). This study’s findings revealed that AI capabilities are vital for information gathering, analyzing, and decision-making in the metaverse context. BCT facilitates ensuring a transparent, visible, traceable, and immutable supply chain, which helps make it more resilient and improves the closed-loop supply chain (CLSC) system with positive technological advancements and significant effects on increasing sustainable business performance (SBP). This study’s findings help organizations understand the potential benefits of AI-enabled SMEs’ presence in the metaverse. The current investigation provides a strategy for managers to gain a competitive advantage, make the supply chain more robust, and enhance overall business performance. Full article

Circular economy (CE) is a model that is gaining significance in the context of sustainable development and environmental protection, focusing on minimizing waste generation and maximizing the use of available resources through recycling and extending product life cycles. The implementation of CE in various European Union countries demonstrates diverse approaches to resource management, waste production, and energy efficiency improvement. These differences primarily stem from varying strategies, national policies, levels of social awareness, and technological advancements. The article identifies the key challenges and barriers associated with CE implementation in selected countries—Poland, the Netherlands, and Romania—and highlights specific areas requiring improvement and adaptation. It emphasizes the critical role of aligning national policies with the EU guidelines, promoting ecological education, and investing in innovative technologies and solutions that support sustainable development. Additionally, it points to the need for developing appropriate waste management infrastructure and encouraging businesses and consumers to change habits and engage in pro-environmental actions. Full article

The integrity of habitat quality is a pivotal cornerstone for the sustainable advancement of local ecological systems. Rapid urbanization has led to habitat degradation and loss of biodiversity, posing severe threats to regional sustainability, particularly in extremely vulnerable arid zones. However, systematic research on the assessment indicators, limiting factors, and driving mechanisms of habitat quality in arid regions is notably lacking. This study takes Urumqi, an oasis city in China’s arid region, as a case study and employs the InVEST and PLUS models to conduct a dynamic evaluation of habitat quality in Urumqi from 2000 to 2022 against the backdrop of land use changes. It also simulates habitat quality under different scenarios for the year 2035, exploring the temporal and spatial dynamics of habitat quality and its driving mechanisms. The results indicate a decline in habitat quality. The habitat quality in the southern mountainous areas is significantly superior to that surrounding the northern Gurbantunggut Desert, and it exhibits greater stability. The simulation and prediction results suggest that from 2020 to 2035, habitat degradation will be mitigated under Ecological Protection scenarios, while the decline in habitat quality will be most pronounced under Business-As-Usual scenarios. The spatial distribution of habitat quality changes in Urumqi exhibits significant autocorrelation and clustering, with these patterns intensifying over time. The observed decline in habitat quality in Urumqi is primarily driven by anthropogenic activities, urban expansion, and climate change. These factors have collectively contributed to significant alterations in the landscape, leading to the degradation of ecological conditions. To mitigate further habitat quality loss and support sustainable development, it is essential to implement rigorous ecological protection policies, adopt effective ecological risk management strategies, and promote the expansion of ecological land use. These actions are crucial for stabilizing and improving regional habitat quality in the long term. Full article

As environmental pollution intensifies, China has begun to implement green credit policies to reduce credit allocation to highly polluting enterprises. This research examines the influence of green credit on the cash flow of heavily polluting enterprises, based on the implementation of the “Green Credit Guidelines”. The policy creates a quasi-natural experimental setting by giving businesses access to an exogenous occurrence. Consequently, this paper uses data from 494 A-share listed companies in China over a fifteen-year period from 2007 to 2021 and employs a Difference-in-Differences (DID) model to assess the net effect of the policy, positing that green credit scheme will prevent highly polluting businesses from making money. The empirical findings show that the green credit policy significantly reduces the cash flow of businesses that emit a lot of pollutants, especially when it comes to operational cash flow. Heterogeneity analysis reveals that the cash flow of high-emission regions and non-state-owned heavily polluting enterprises is affected even more significantly. Previous research has often overlooked cash flow as a metric; however, cash flow is a critical indicator of an enterprise’s operational status. From this angle, this study adds to our knowledge of how green credit schemes affect highly polluting businesses. Additionally, it contributes to the ongoing discussion regarding the relationship between financial constraints and cash flow. China’s government ought to keep encouraging the creation of green credit regulations, enhance supervision of state-owned heavily polluting enterprises, and pay attention to low-emission regions by establishing dynamic regulatory indicators to promote ecological civilization construction and the transformation and upgrading of lagging industries. Full article

In today’s data-driven business landscape, effective and transparent decision making becomes relevant to maintain a competitive advantage over the competition, especially in customer service in B2B environments. This study presents a methodological framework that integrates Explainable Artificial Intelligence (XAI), C-means clustering, and the Analytic Hierarchical Process (AHP) to improve strategic decision making in business environments. The framework addresses the need to obtain interpretable information from predictions based on machine learning processes and the prioritization of key factors for decision making. C-means clustering enables flexible customer segmentation based on interaction patterns, while XAI provides transparency into model outputs, allowing support teams to understand the factors influencing each recommendation. The AHP is then applied to prioritize criteria within each customer segment, aligning support actions with organizational goals. Tested with real customer interaction data, this integrated approach proved effective in accurately segmenting customers, predicting support needs, and optimizing resource allocation. The combined use of XAI and the AHP ensures that business decisions are data-driven, interpretable, and aligned with the company’s strategic objectives, making this framework relevant for companies seeking to improve their customer service in complex B2B contexts. Future research will explore the application of the proposed model in different business processes. Full article

The COVID-19 pandemic offers an opportunity for the revitalisation of empty Spain and the development of new sustainable business models in a healthier environment, taking the competitive advantages of digitalisation and the benefits of contact with nature. This study presents a positive analysis of the situation after three months of confinement with the research objective of evaluating the potential for development a sustainable social economy in empty Spain based on the hypotheses presented. In order to demonstrate the six hypotheses put forward in the research, a review of the existing literature was conducted, socio-economic and environmental indicators from official sources were consulted, and descriptive statistics methods have been applied. Digitalisation, the social economy, the bio-economy, and the revitalisation of heritage seem to be the drivers for achieving the challenges proposed. By perceiving reality through a lens that values nature and creative intelligence, a new avenue of opportunities may be opened up, leading to an improvement in quality of life and well-being, and potentially retaining the rural population. Following this study, which assesses the opportunities, risks, and challenges and establishes a plan of measures, players, and resources for future implementation in vacant Spain, new lines of work will become available. Full article

In recent years, businesses have faced growing pressures from stakeholders, including investors, customers, and regulators, to adopt sustainable practices. These pressures stem from the global focus on environmental, social, and governance (ESG) criteria and their association with risk management and corporate resilience. As a result, understanding the connection between sustainability and performance indicators, such as return on equity (ROE) and return on assets (ROA), is crucial to determine whether sustainable practices positively influence financial outcomes or primarily serve to address external expectations. This study seeks to bridge the gap between theoretical frameworks and empirical evidence by employing a rigorous methodological approach—Structural Equation Modeling (SEM)—to assess the impact of sustainability practices on key performance measures. The inclusion of a diverse range of industries from the US and Europe enhances the relevance of the findings, as it facilitates their generalization across developed economies where sustainability initiatives are highly prioritized. Our results are consistent with prior research demonstrating a positive relationship between sustainability and financial performance, particularly in high-development contexts over a medium-term period. These findings carry important implications for managers and policymakers, emphasizing that sustainability is not a compromise but a catalyst for economic and financial profitability. This study contributes to the literature by illustrating how sustainability can simultaneously advance ethical objectives and enhance financial performance, establishing it as a critical area of focus for both academics and practitioners. Full article

In the pursuit of development and evaluation, entrepreneurship is the key to unlocking the shackles of poverty and unemployment. However, Pakistani students often catch themselves lacking the catalyst that ignites the entrepreneurial flame due to the absence of the essential knowledge, skills, and creative mindset, which can lead to significant challenges and a dim attitude toward entrepreneurial intentions. The harsh reality is that the rate of new business formation in Pakistan is only 1.5%. Therefore, the study aims to shed light on the impact of entrepreneurial inspiration, entrepreneurial skills, and entrepreneurial awareness on the intentions of Pakistani university students with the mediating effects of entrepreneurial education. Revealing the importance of entrepreneurial education in shaping the minds and desires of the students, seven hypotheses were developed based on social cognitive theory (SCT). A comprehensive survey was used to collect primary data from final-year business students, with a sample size of 865, from seven universities in the Punjab region of Pakistan that provide higher education in entrepreneurship and the data were analyzed in two stages, namely a model measurement and structural model Via Smart PLS 4. The findings clearly augment entrepreneurial inspiration, entrepreneurial skills, and entrepreneurial awareness, which can ignite the flames of entrepreneurship among Pakistani students. Moreover, entrepreneurial education plays a crucial role in mediating relationships and motivating students to choose entrepreneurship as a future occupation. However, the study is restricted to the Punjab region of Pakistan and focuses solely on final-year business students. This study serves as a guiding light of hope for academics, policymakers, and educational institutions to emphasize the need to revamp the business education landscape in Pakistan to foster entrepreneurial passion among our future leaders. Full article

In the context of global climate change and urbanization, enhancing the resilience of rural tourism is crucial for achieving rural revitalization and fostering sustainable development. This study uses Hala New Village in Heilongjiang Province, China, as a case study to analyze the dynamic stages of tourism resilience development and its evolutionary mechanisms, applying the Actor Network Theory to provide strategies for enhancing rural tourism resilience. The study finds the following: (1) The evolution of tourism resilience in Hala New Village has progressed through three phases: the budding and vulnerability phase, the groping and resistance phase, and the optimization and adaptation phase. (2) The turnover of key actors, the change of actor types, the complexity of translation processes, and the integration and innovation of business models are the surface mechanisms driving the evolution of rural tourism resilience. (3) The deep mechanisms underlying this evolution are shaped by the synergistic effects of various internal and external factors, such as the national rural development strategy, environmental emergencies, market-driven changes in tourist demand following COVID-19, external capital interventions, and the inherent potential of Hala New Village’s natural and cultural environment. This study not only advances theoretical research on rural tourism resilience and actor networks but also offers practical insights for regions worldwide where rural tourism remains underdeveloped. Full article

With the rapid development of prominent models, NL2SQL has made many breakthroughs, but customers still hope that the accuracy of NL2SQL can be continuously improved through optimization. The method based on large models has brought revolutionary changes to NL2SQL. This paper innovatively proposes a new NL2SQL method based on a large language model (LLM), which could be adapted to an edge-cloud computing platform. First, natural language is converted into Python language, and then SQL is generated through Python. At the same time, considering the traceability characteristics of financial industry regulatory requirements, this paper uses the open-source big model DeepSeek. After testing on the BIRD dataset, compared with most NL2SQL models based on large language models, EX is at least 2.73% higher than the original method, F1 is at least 3.72 higher than the original method, and VES is 6.34% higher than the original method. Through this innovative algorithm, the accuracy of NL2SQL in the financial industry is greatly improved, which can provide business personnel with a robust database access mode. Full article

Seaplane operations connect remote areas, promote tourism, and provide unique transportation solutions. After many years of preparations, commercial seaplane operations on a network of 100 water airports and 200 waterways in Greece are about to commence. The network can serve the needs of 1.6 million permanent residents of the Greek islands, the inhabitants of the mainland, and over 35 million annual tourists. This paper aims to conduct a PESTLE (Political, Economic, Social, Technological, Legal, and Environmental) analysis to identify the factors that have delayed operations and those that will affect the success of future operations. As such, 26 factors are examined. It was found that the Greek debt crisis and the COVID-19 pandemic were impediments to operations. The potential of using electric seaplanes is discussed. Recent developments in using drone inspection capabilities for aviation safety are examined. Management strategies for the Etesian winds and other environmental issues are presented. Overall, seaplane operations have enormous potential, while the Greek economic recovery provides favorable conditions for completing the project. The critical issue determining success is executing a multi-faceted business model to ensure seaplane operations’ financial viability. The network can act in synergy with other modes of transportation to help achieve social cohesion, improve tourism services, and foster economic development. Full article