Search Results (4,217)

With the rapid growth of electric vehicles (EVs) and renewable energy generation, Vehicle-to-Grid (V2G) technology has emerged as a promising approach for transforming EVs from passive charging loads into flexible distributed energy storage resources. By enabling bidirectional power exchange between EV batteries and the power grid, V2G can support renewable energy accommodation, peak shaving, demand response, ancillary services, and local grid balancing. This review provides a systematic synthesis of recent advances in V2G systems for renewable energy integration, with particular emphasis on coordinated scheduling, economic mechanisms, battery degradation, and user engagement. First, the technical foundations of V2G are introduced, including Vehicle-to-Everything operating modes, bidirectional charging architecture, aggregation mechanisms, grid-support services, and renewable accommodation pathways. Second, major scheduling strategies are reviewed, including price-based, load-based, renewable-forecast-driven, centralized, distributed, and hybrid approaches. Third, the economic feasibility of V2G is examined from the perspectives of revenue streams, pricing mechanisms, business models, battery aging costs, and compensation schemes. In addition, user participation barriers, such as range anxiety, battery lifetime concerns, loss of control, uncertain financial returns, and data privacy, are discussed. Key challenges related to communication standards, interoperability, cybersecurity, market access, policy design, and pilot-scale validation are also summarized. Finally, future development directions are identified, including AI-based scheduling, aggregator platforms, fleet-scale V2G, degradation-aware optimization, carbon-aware electricity markets, and user-centered participation mechanisms. This review highlights that large-scale V2G deployment requires the integrated coordination of technical scheduling, economic incentives, battery health protection, and user acceptance in renewable-rich power systems. Full article

Large Language Models (LLMs) have rapidly become central components of cognitive computing systems and AI-assisted knowledge work. However, the effectiveness of LLM-generated outputs depends not only on the model’s capabilities but also on the structure of the prompts used to guide them. This study investigates how structured prompting techniques influence perceived output usefulness in business-oriented tasks. First, we conduct a systematic literature review following PRISMA guidelines to identify, classify, and synthesize existing prompt enhancement strategies. The review leads to the development of a taxonomy distinguishing task-alignment techniques (e.g., one-shot and few-shot prompting) from reasoning-transparency techniques (e.g., Chain-of-Thought prompting). Building on this taxonomy, we design a controlled experimental study in which knowledge workers evaluate LLM-generated outputs across analytical and summarization tasks. Using linear mixed-effects modeling, we assess the impact of prompting techniques and the moderating role of Generative AI usage frequency. Results indicate that structured prompting significantly increases perceived usefulness compared to baseline approaches, with the combination of example-based conditioning and explicit reasoning scaffolding yielding the highest evaluations. The moderating effect of usage frequency is not statistically significant, suggesting that the benefits of structured prompt design are robust across different experience levels. These findings position prompt structure as a practical cognitive interface mechanism and provide evidence-based guidelines for enhancing human–AI interaction in cognitive computing environments. Full article

With rapid urbanization and increasing motorization, understanding the impact of parking facilities on urban metro ridership is crucial for alleviating traffic congestion and promoting public transport priority strategies. However, few studies have systematically examined the influence of built environment characteristics, especially parking facilities, on metro ridership. To address this research gap, this study utilizes metro ridership data and parking facility data, and employs a gradient boosting regression tree (GBRT) model to analyze the relationship between built environment factors (including parking) and metro ridership. Additionally, accumulated local effects (ALE) plots are used to reveal nonlinear effects and interaction patterns. The empirical results demonstrate that parking space density has a significant nonlinear influence on metro ridership. Furthermore, a clear threshold effect is observed in the joint impact of parking space density and distance to the central business district (CBD), providing valuable theoretical and practical insights for optimizing park and ride (P+R) facility planning around metro stations. These findings contribute to a deeper understanding of the interplay between built environment factors and metro ridership, offering evidence-based guidance for sustainable urban transport planning. Full article

Based on attachment theory, individuals develop relational schemas that shape cognitive-emotional social relationship expectations (e.g., others are a source of safety). Social relationships (e.g., intimate relationships or close friendships) are a source of long-term happiness. However, expectations that they will save someone from life’s challenges are a common fallacy (e.g., a shining prince/princess bringing lifelong happiness). This places illusionary expectations on others to not disappoint despite normal behavioral realities (e.g., relational misunderstandings and conflict). This project psychometrically developed the rescue fantasy beliefs (RFB) and expected relational disappointment (ERD) scales. Analysis of the scales demonstrated satisfactory reliability, discriminant validity, and convergent validity. Serial mediation analysis demonstrated that higher RFB is associated with higher shopping addiction. ERD and current relational satisfaction sequentially mediated this relationship. The results demonstrated a serial connection between RFB and lower ERD. This serial illusionary expectation gap in others is associated with lower current relational satisfaction and higher shopping addiction. Addictive shopping can function as a compensatory coping strategy to unmet social needs. Business marketing implications discuss how new offerings can encourage meaningful in-person social connections to better address underlying needs (for those with greater RFB). Full article

Within the food industry, research on mobile gastronomy has increased from the consumer perspective. Food trucks play an important role as economic units worldwide, serving as a culinary alternative to traditional restaurants. They have emerged as an innovative initiative and business model that offers a disruptive alternative to home cooked meals. One of the aspects most appreciated by consumers is the charcoal-grilled food offered by food trucks. Globally, charcoal is widely used as an energy source and cooking fuel, with an annual production of approximately 53.2 million tons. Its characteristics and low cost make charcoal a dominant energy resource, and it plays a fundamental role in cooking in both low- and high-income countries due to the distinctive flavor and texture it imparts to food. Research has focused on air pollution and health risks, supplemented with information on the types of charcoal, characteristics and properties, production techniques, and added value. Charcoal ash residue production has not been fully analyzed, providing an opportunity for research to obtain data and evaluate various criteria, such as kilograms of charcoal purchased and food trucks’ residual charcoal ash. To address this gap, the authors propose a horizontal collaboration perspective through the application of the COHRV model to (1) collect data and create a database from food-truck business owners in Ciudad Juarez, Chihuahua; (2) develop a circular economy model for charcoal ash as a sustainable strategy within the food industry; and (3) estimate charcoal consumption during the grilling process and the generation of charcoal ash residue in the food truck sector. Full article

Adventure tourism businesses operate in environments characterized by substantial physical risk, highly dynamic natural conditions, and rising customer expectations. As demand for adventure tourism grows, operators must continuously balance experiential delivery with effective risk management and operational control. This study investigates safety management practices among adventure tourism operators in Norway. Data were collected via an online survey of 89 commercial adventure tourism businesses. Results show that environmental conditions—particularly rapidly changing weather—represent the most commonly perceived risk factor, followed by client behaviour, including failure to follow instructions. Slips, trips, and falls constitute the most frequently reported injury type. Tour guide competence emerges as a central safety management strategy. Collectively, the findings underscore the crucial role of tour guides as frontline safety managers and illustrate how small adventure tourism operators in Norway maintain robust safety systems within the constraints of limited resources. The study contributes to the literature on business management in high-risk service sectors and provides practical insights for strengthening safety management practices in adventure tourism. Full article

In addressing the pivotal challenges of food security and environmental sustainability in China, this investigation rigorously assesses the lifecycle ramifications of quintuple straw utilization methodologies in Xinyang. Utilizing the comprehensive framework of life cycle assessment, the study meticulously examines a spectrum of twelve environmental indicators within a multitude of scenarios. Open-air straw burning shows a global warming potential (GWP) of 0.861 kg CO₂-eq/kg straw, while soil incorporation and feed production yield net-negative GWP of −0.287 kg CO₂-eq and −0.270 kg CO₂-eq, respectively, demonstrating substantial climate mitigation benefits. Scenario simulations further indicate that redirecting unused straw to soil incorporation or feed production reduces regional GWP intensity by 15–25% versus business as usual, whereas prioritizing energy recovery delivers the greatest reductions in fine particulate matter formation and human toxicity. This research underscores the substantial environmental advantages that can be harnessed through the enhanced application of straw utilization strategies in Xinyang. It thoughtfully considers an array of factors, including the local availability of straw, prevailing usage patterns, and the intricacies of technical processes. Full article

Maintenance plays an increasingly strategic role across industrial sectors, influencing not only asset availability and operational efficiency but also environmental and social performance. However, assessing the sustainability of maintenance practices remains a cross-disciplinary challenge due to the absence of integrative, broadly applicable evaluation frameworks. This study introduces the Tripartite Business Model Canvas (T-BMC), a novel diagnostic instrument that reconceptualises maintenance as a business model structured around the Triple Bottom Line. By embedding each of the nine Business Model Canvas blocks within the economic, environmental, and social dimensions, the T-BMC yields 27 analytical elements, operationalised through a 24-item assessment instrument after three design consolidations to avoid construct redundancy. The instrument supports maturity assessment, benchmarking, and continuous improvement across diverse industrial contexts. An exploratory pilot study with 17 maintenance experts from the Chilean seawater desalination sector assessed its applicability, internal coherence, and contextual relevance. The overall perceived maturity level was 3.96 out of 5, with the Social dimension scoring highest (4.10) and the Economic dimension lowest (3.88); at the block level, Key Activities (4.27) and Value Propositions (4.10) were the strongest areas, while Channels (3.74) and Revenue Streams (3.78) revealed the main sustainability gaps. Internal consistency was strong (Cronbach’s α = 0.94 overall; 0.85, 0.82, and 0.89 for the economic, environmental, and social subscales), although, given the sample size (n = 17), these findings constitute preliminary evidence rather than confirmatory validation. A Maintenance Sustainability Dashboard further translates diagnostic outputs into actionable visual insights for decision-making and cross-plant benchmarking. These contributions offer a structured, transferable pathway for embedding sustainability within maintenance strategy and a basis for future quantitative indicator development and large-scale validation. Full article

Customer churn has become a major challenge in the retail industry, where customer loyalty directly affects business success and sustainability. Despite the significant progress in Artificial Intelligence, especially in prediction tasks, its use in the retail e-commerce domain remains limited and underexplored; this is due to the scarcity and limited quality of available datasets. To address these challenges, this paper proposes a churn prediction approach designed to handle data scarcity while ensuring accurate performance. We experimented with a combination of various feature selection techniques along with several Machine Learning and Deep Learning models to evaluate their performance on a limited tabular dataset. The impact of feature selection on predictive performance was also systematically analyzed. The results demonstrated that feature selection plays an important role in improving model performance by identifying the key features that have the most significance to the classification task. The analysis showed that the L1-based Logistic Regression feature selection method combined with the Extreme Gradient Boosting classifier achieved the best performance, with a Macro F1-score of 95.25%. Based on these results, companies can identify potential churners and implement retention strategies. These findings may provide a useful reference point for future researchers in the retail e-commerce industry. Full article

Against the backdrop of the global green transition and China’s pursuit of high-quality opening-up, examining whether green industrial policy can improve firms’ OFDI performance is important for understanding how domestic green transformation can be translated into international competitive advantages. Firms’ OFDI performance is not only related to the long term returns of firms’ cross border operations, but also reflects the effectiveness of a country’s opening up strategy. Using Chinese A share listed manufacturing firms from 2010 to 2024 as the research sample, this study constructs a multi period difference in differences model to systematically examine the impact of Green Factory certification on firms’ OFDI performance. The results show that Green Factory certification significantly improves firms’ OFDI performance. This conclusion remains robust after a series of tests, including the parallel trend test, heterogeneous treatment effect identification, and instrumental variable estimation. Further mechanism analysis indicates that Green Factory certification promotes the improvement of OFDI performance mainly through two channels, namely enhancing total factor productivity and strengthening the green innovation-driven effect. The moderating effect results show that overseas investment experience strengthens the positive effect of Green Factory certification on OFDI performance. The heterogeneity analysis further suggests that the performance-enhancing effect is more evident among firms adopting wholly owned entry modes, firms operating in heavily polluting industries, non-state-owned firms, and firms investing in host countries with higher environmental performance. This study contributes to the literature on environmental regulation, green innovation, and international business by showing that Green Factory certification functions not only as a green industrial policy tool, but also as both an institutional signal and a capability-building mechanism, helping to convert firms’ green transformation capabilities into international performance advantages. Full article

Digital transformation has become a critical pathway for manufacturing firms seeking to improve responsiveness, resource efficiency, and long-term sustainability. However, existing studies have paid limited attention to how digital transformation strategies generate organizational agility across different stages of sustainable manufacturing transformation. Drawing on dynamic capability theory, this study develops a stage-contingent Strategy–Ambidexterity–Agility framework and conducts a longitudinal single-case study of Contemporary Amperex Technology Co., Limited (CATL) from 2011 to 2023. The findings show that organizational agility develops cumulatively through three transformation stages. In the initial stage, a lean-oriented strategy supports balanced ambidexterity and cultivates customer agility through production optimization. In the development stage, an enhancement-oriented strategy enables exploitation-dominant combined ambidexterity and builds market agility through cross-functional integration and closed-loop business logic. In the industry-leading stage, a leap-oriented strategy supports exploration-dominant combined ambidexterity and fosters value chain agility through ecosystem orchestration, intelligent operations, and circular value creation. This study contributes to the literature on digital transformation and sustainable manufacturing by showing how stage-contingent digital strategies shape ambidexterity configurations, generate layered agility capabilities, and support sustainability-oriented manufacturing outcomes. Full article

This research examines the tourism structure of Santorini within the framework of the Triple Layered Business Model Canvas (TLBMC) and evaluates how circular tourism principles can be integrated into the value creation process across the TLBMC layers. The study utilizes secondary sources such as tourism reports, policy documents, corporate publications, and previous academic studies. Conducted using qualitative case study principles, this study employs PESTLE analysis, stakeholder analysis, TLBMC framework analysis, alignment analysis, and SWOT–CAME analysis. The research concludes that tourism in Santorini is highly dependent on a premium positioning strategy and characterized by a cruise- and experience-oriented visitor structure. Furthermore, the existing tourism structure exhibits both environmental and social vulnerabilities, including water scarcity, overtourism, seasonal demand concentration, and housing pressure. The destination possesses significant potential for stronger alignment among economic, environmental, and social layers, particularly through the introduction of alternative tourism activities. This research provides significant contributions to the literature and offers recommendations for all stakeholders, especially local actors, to establish a resilient tourism model that supports long-term alignment among the layers. Full article

In the context of rapid urbanization, the traditional spatial fabric and cultural connotations of historic districts are increasingly threatened, leading to growing problems such as architectural homogenization and weakened public identity. As an important dimension linking spatial form and public cognition, urban memory has gradually become a key entry point for the study of historic district conservation and renewal. At the same time, the large volume of user-generated content accumulated on social media provides a new data foundation and research pathway for architectural and urban memory studies. Taking the Sajinqiao area in Xi’an as the study area, this study uses Weibo texts containing the keyword “Sajinqiao” from 2018 to 2025 as the basic dataset. A Chinese-RoBERTa pretrained language model was employed to identify and screen high-focus Weibo samples, and a classification framework of five types of memory elements was constructed, including roads, areas, nodes, business units, and food entities. On this basis, memory elements were extracted, standardized, and quantified in terms of memory intensity to analyze their evolutionary characteristics. The results show that, first, urban memory in the Sajinqiao area exhibited marked stage-based fluctuations during the study period. Second, business- and consumption-related elements remained dominant in the type structure over the long term. Third, core urban memory was primarily supported by local food entities and related business units, indicating that public memory gradually shifted from experience-oriented memory to destination-oriented memory. This study provides an operational framework for the identification, quantification, and dynamic assessment of urban memory in historic districts, and offers empirical support for memory-oriented conservation and renewal strategies in the Sajinqiao area and similar historic districts. Full article

Accurately assessing the multifaceted performance of urban commercial land is essential for steering consumption upgrading and refining urban spatial planning. However, existing scholarship continues to treat commercial land through a predominantly economic lens; non-economic indicators have rarely been compared with price-based metrics within a unified analytical framework. To bridge this gap, this study constructs a multidimensional benefit evaluation framework encompassing four indicators, namely, land values, shop rent, online reviews, and customer ratings, across 10,766 commercial land parcels in Chengdu, China, and applies XGBoost-SHAP methods to examine their predictive associations and spatial patterns. This study reveals the differences and commonalities among the four benefit indicators in terms of statistical characteristics, spatial distribution, and matching patterns, and further examines the ranking of their key predictors, as well as the nonlinear effects, threshold effects, and interaction effects with key factors. These findings corroborate the distinctiveness of the four benefit variables and the significance of comprehensively evaluating commercial land-use benefits. This study provides novel perspectives and empirical evidence for assessing commercial land-use benefits and their predictive mechanisms, and offers actionable guidance for formulating targeted consumption development, spatial layout, and business format optimization strategies, thereby contributing to the sustainable vitality of urban economic spaces. Full article

Artificial intelligence (AI) serves as a vanguard technology in the modern epoch, playing an essential part in fostering ecological and sustainable progress. By utilizing longitudinal data from Chinese A-share corporations between 2014 and 2023, this inquiry empirically explores how AI integration affects the green innovation bubbles of firms along with the governing mechanisms. Our evidence reveals that AI adoption exerts a significant inhibitory effect on such bubbles; for every one-standard-deviation uptick in AI utilization, there is a corresponding decline in green innovation bubbles of approximately 0.108 standard deviations. This finding remains robust across multiple robustness checks. Mechanism analysis shows that AI mitigates green innovation bubbles by enhancing green total factor productivity and reducing excessive managerial expenses. Furthermore, the expansion of the digital financial landscape and the exploitation of information assets bolster the repressive influence of artificial intelligence. Analytical tests for heterogeneity demonstrate that this influence is more significant for state-controlled corporations, businesses operating in non-polluting industries, and those headquartered within the eastern regions of China. Overall, the findings provide robust empirical evidence that AI adoption contributes to the governance of inefficient and inflated green innovation activities, while the causal interpretation of the results should remain cautious given the observational nature of the data and the limitations of the identification strategy. Full article