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The goals of the European Union towards the energy transition imply profound changes in the energy field, so as to promote sustainable energy development while fostering economic growth. To achieve these changes, the incorporation of sustainable technologies supporting decentralisation, energy efficiency, renewable energy production, and demand flexibility is of vital importance. Blockchain has the potential to change energy services towards this direction. To optimally exploit blockchain, innovative business models need to be designed, identifying the opportunities emerging from unmet needs, while also considering potential risks so as to take action to overcome them. In this context, the scope of this paper is to examine the opportunities and the risks that emerge from the adoption of blockchain in four innovative business models, while also identifying mitigation strategies to support and accelerate the energy transition, thus proposing optimal approaches of exploitation of blockchain in energy services. The business models concern Energy Performance Contracting with P4P guarantees, improved self-consumption in energy cooperatives, energy efficiency and flexibility services for natural gas boilers, and smart energy management for EV chargers and HVAC appliances. Firstly, the value proposition of the business models is analysed and results in a comprehensive SWOT analysis. Based on the findings of the analysis and consultations with relevant market actors, in combination with the examination of the relevant literature, risks are identified and evaluated through a qualitative assessment approach. Subsequently, specific mitigation strategies are proposed to address the detected risks. This research demonstrates that blockchain integration into these business models can significantly improve energy efficiency, reduce operational costs, enhance security, and support a more decentralised energy system, providing actionable insights for stakeholders to implement blockchain solutions effectively. Furthermore, according to the results, technological and legal risks are the most significant, followed by political, economic, and social risks, while environmental risks of blockchain integration are not as important. Strategies to address risks relevant to blockchain exploitation include ensuring policy alignment, emphasising economic feasibility, facilitating social inclusion, prioritising security and interoperability, consulting with legal experts, and using consensus algorithms with low energy consumption. The findings offer clear guidance for energy service providers, policymakers, and technology developers, assisting in the design, deployment, and risk mitigation of blockchain-enabled business models to accelerate sustainable energy development. Full article

This paper selects 50 listed commercial banks in China from 2012 to 2023 as research samples, and employs the fixed effects model and Hansen’s threshold regression method to systematically examine the impact mechanism and non-linear characteristics of FinTech development on the profitability of commercial banks. The key findings are summarized as follows: (1) FinTech significantly undermines the overall profitability of commercial banks by reshaping the competitive landscape of the industry and intensifying the technology substitution effect. This is primarily reflected in the reduction in traditional interest income and the erosion of market share in intermediary business. (2) Heterogeneity analysis indicates that large state-owned banks and joint-stock banks experience more pronounced negative impacts compared to small and medium-sized banks. (3) Additional research findings reveal a significant single-threshold effect between FinTech and bank profitability, with a critical value of 4.169. When the development level of FinTech surpasses this threshold, its inhibitory effect diminishes substantially, suggesting that after achieving a certain degree of technological integration, commercial banks may partially alleviate external competitive pressures through synergistic effects. This study offers crucial empirical evidence and theoretical support for commercial banks to develop differentiated technology strategies and for regulatory authorities to design dynamically adaptable policy frameworks. Full article

The rapid growth of live e-commerce, a sector valued at over USD 100 billion worldwide, demonstrates its transformative impact on the retail industry, especially in markets like China, where platforms such as Taobao Live and TikTok Shop have markedly altered consumer interaction. This transition is further expedited by Netflix-like entertainment marketing methods, which have demonstrated the capacity to enhance consumer retention by as much as 40%. As organizations adjust to this evolving landscape, it is essential to optimize supply chain strategies to align with these dynamic, consumer-centric environments. This paper examines the complexity of decision-making in live e-commerce supply chains, specifically regarding Netflix-inspired marketing strategies. The primary aim of this study is to design a game-theoretic framework that examines the interactions between producers and online celebrity retailers (OCRs) across different power dynamics. As live commerce integrates digital retail with immersive experiences, businesses must optimize pricing, quality, and marketing strategies in real-time. We present engagement-driven marketing as a strategic variable and incorporate consumer regret and switching costs into the demand function. To illustrate practical trade-offs in strategy, we incorporate a multi-criteria decision-making (MCDM) layer with AHP-TOPSIS, assessing profit, consumer surplus, engagement score, and channel efficiency. The experiment results indicate that Netflix-style marketing markedly increases demand and profit in retailer-led frameworks, whereas centralized tactics enhance overall channel performance. TOPSIS analysis prioritizes high-effort, high-engagement methods, whereas the Stackelberg experiment underscores the influence of power dynamics on profit distribution. This study presents an innovative integrative decision-making methodology for enhancing live-streaming commerce tactics in data-driven and consumer-focused markets. Full article

This study intends to provide insights and challenges for the shape of the B movement, an emerging paradigm that fosters cross-sectoral partnerships and encourages ethical business practices through so-called purpose-driven businesses. Focusing on Italy, the first European country to adopt this managerial model, the research investigates Italian Benefit Corporations, known as Società Benefit (SB), and their most appealing sustainability claims from a consumer perspective. The analysis intends to inform theory development by assuming the cultural and creative industry (CCI) as a field of interest, utilizing a within-subjects experimental design to analyze data from a diverse consumer sample across various contexts. The results indicate that messaging centered on economic sustainability emerged as the most effective in generating positive consumer responses, highlighting a prevailing inclination toward pragmatic factors such as affordability, economic accessibility, and tangible benefits rather than social issues. While sustainable behaviors are not yet widespread, latent ethical sensitivity for authentic, value-driven businesses suggests that economic and ethical dimensions can be strategically synthesized to enhance consumer engagement. This insight highlights the role of BCs in catalyzing a shift in consumption patterns within ethical-based and creative-driven sectors. Full article

Freemium, a popular business model in the digital economy, offers a basic product for free while charging for advanced features or value-added services. This pricing strategy enables platforms to attract a broad user base and then monetize through premium offerings. Customer characteristics and service price are important factors affecting customer choice behavior in such a model. Based on consumption stickiness, we consider a monopoly that provides value-added services by incorporating a multinomial logit model into a two-stage dynamic pricing model. First, we analyze the optimal pricing of value-added services under a normal sales scenario. We then consider optimal pricing during the marketing period under two strategies—level improvement for value-added services and quality reduction for a basic product—and analyze the applicability of each. The results show that increasing the value-added service level has a positive effect on the optimal price of value-added services, whereas reducing the basic product quality has no effect on the optimal price. Furthermore, the numerical simulation shows that when the depth of consumer stickiness is low, the optimal marketing strategy reduces the quality of the basic product, the price of value-added services should be higher than that in the normal sales period but lower than the price under the level-improvement strategy for value-added services; otherwise, improving the level of the value-added services becomes the optimal approach. This study provides a theoretical basis and decision support for product quality design and service pricing that applies to freemium platforms. Full article

A reliable geothermal risk assessment methodology is key to any business decision. To be effective, it must be based on widely accepted principles, be easy to apply, be auditable, and produce consistent results. In this paper, we review the key characteristics of a geothermal project and propose a novel approach derived from risk and uncertainty definitions used in the hydrocarbon industry. According to the proposed methodology, the probability of success is assessed by estimating three different components. The first is the geological probability of success, which is the likelihood that the geological model on which the geothermal project is based is correct and that the key fundamental geological elements are present. The second, the temperature threshold, is defined as the probability that the fluid is above a certain reference value. Such a reference value is the one used to design the development. Such a component, therefore, depends on the end use of the geothermal resource. The third component is the commercial probability of success and estimates the chance of a project being commercially viable using the Reverse Enthalpy Methodology. Geothermal projects do not have a single parameter that represents their monetary value. Therefore, in order to estimate it, it is necessary to make an initial assumption that can be revisited later in an iterative manner. The proposed methodology works with either the capital expenditure of the geothermal facility (power plant or direct thermal use) or the drilling cost as the initial assumption. Varying the other parameter, it estimates the probability of having a net present value (NPV) higher than zero. Full article

Having in view the current global disruptive social and economic landscape, sustainability becomes more important than ever. As producers become more concerned about adopting more sustainable practices, customer awareness towards sustainable behavior must be the focus of all stakeholders. Within this context, the SHIFT framework (proposed in 2019) highlights the manner in which consumers’ traits and attitudes influence their propensity towards sustainable consumption. It consists of five factors considered to be relevant to consumer behavior: Social influence, Habit formation, Individual self, Feelings and cognition, and Tangibility. Different from previous studies, this research focuses on applying the SHIFT framework to the automotive industry, taking into consideration the contribution of digital technologies to fostering sustainable consumer behavior throughout the entire product lifecycle. Using a qualitative research approach, the most relevant digital technologies in the automotive industry were identified and mapped in relation to the three phases of consumption (choice, usage, and disposal). The research aimed to develop and test an original conceptual framework, starting from the SHIFT. The results of the study highlight the fact that the digital technologies, in their diversity, are integrated in different ways into each of the three phases, facilitating the adoption of sustainable consumption. To achieve sustainability, the two key stakeholders, consumers and producers, should share a common ground on capitalizing the opportunities offered by digital technologies. Full article

The introduction of mobile banking is essential in today’s financial sector, where technological innovation plays a critical role. To remain competitive in the current market, businesses must analyze client attitudes and perspectives, as these influence long-term demand and overall profitability. While previous studies have explored general adoption behaviors, limited research has examined how individual factors such as social influence, lifestyle compatibility, financial technology self-efficacy, and perceived usage cost affect mobile banking adoption among specific generational cohorts. This study addresses that gap by offering insights into these variables, contributing to the growing literature on mobile banking adoption, and presenting actionable recommendations for financial institutions targeting younger market segments. Using a structured questionnaire survey, data were collected from both users and non-users of mobile banking among the Gen Z population in the United States. The regression model significantly predicts mobile banking adoption, with an intercept of $-0.548$ ($p < 0.001$). Among the independent variables, perceived cost of usage has the strongest positive effect on adoption ($B=0.857$, $\beta =0.722$, $p < 0.001$), suggesting that adoption increases when mobile banking is perceived as more affordable. Social influence also has a significant positive impact ($B=0.642$, $\beta =0.643$, $p < 0.001$), indicating that peer influence is a central driver of adoption decisions. However, self-efficacy shows a significant negative relationship ($B=-0.343$, $\beta =-0.339$, $p < 0.001$), and lifestyle compatibility was found to be statistically insignificant ($p=0.615$). These findings suggest that reducing perceived costs, through lower fees, data bundling, or clearer communication about affordability, can directly enhance adoption among Gen Z consumers. Furthermore, leveraging peer influence via referral rewards, Partnerships with influencers, and in-app social features can increase user adoption. Since digital self-efficacy presents a barrier for some, banks should prioritize simplifying user interfaces and offering guided assistance, such as tutorials or chat-based support. Future research may employ longitudinal designs or analyze real-life transaction data for a more objective understanding of behavior. Additional variables like trust, perceived risk, and regulatory policies, not included in this study, should be integrated into future models to offer a more comprehensive analysis. Full article

This study investigates the foundational causes of unsustainability that obstruct efforts to address global challenges such as climate change, environmental degradation, water crises, and public health deterioration. Using qualitative research with in-depth expert interviews from education, environmental studies, and business, it finds that these global challenges, while visible on the surface, are deeply rooted in worldviews that shape human behavior, societal structures, and policies. Building on this insight, the thematic analysis manifests three interrelated systemic paradigms as the fundamental drivers of unsustainability: a crisis of wholeness, reflected in fragmented identities and collective disorientation; a disconnection from nature, shaped by human-centered perspectives; and the influence of dominant political-economic systems which prioritize growth logics over ecological and social concerns. These paradigms underlie both structural and cognitive barriers to systemic transformation, which influence the design and implementation of education for sustainability. By clarifying a body of knowledge and systemic paradigms regarding unsustainability, this paper calls for transformative education that promotes a holistic, value-based approach, eco-empathy, and critical thinking, aiming to equip future generations with the tools to challenge and transform unsustainable systems. Full article

Data visualization is a key activity in data-driven decision making and is gaining momentum in many organizational contexts. However, the role and contribution of both end-user development (EUD) and artificial intelligence (AI) technologies for data visualization and analytics are still not clear or systematically studied. This work investigates how effectively AI-supported EUD tools may assist visual analytics tasks in organizations. An exploratory case study with eight interviews with key informants allowed a deep understanding of data analysis and visualization practices in a large Italian company. It aimed at identifying the various professional roles and competencies necessary in the business context, understanding the data sources and data formats exploited in daily activities, and formulating suitable hypotheses to guide the design of AI-supported EUD tools for data analysis and visualization. In particular, the results of interviews with key informants yielded the development of a prototype of an LLM-based EUD environment, which was then used with selected target users to collect their opinions and expectations about this type of intervention in their work practice and organization. All the data collected during the exploratory case study finally led to defining a set of design guidelines for AI-supported EUD for data visualization. Full article

The effective design of logistics platform business models is an important means for platform-type logistics enterprises to gain a competitive advantage. This study employs RRS Logistics as a case study to clarify the dynamic environmental mechanisms of logistics platform business models from the perspective of value co-creation and build a novel structural framework for logistics platform business models with community at their core. The research findings are as follows: First, guided by the idea of “value positioning–value co–creation–value support–value maintenance–value capture”, the conceptual framework of business models is redefined. The key steps in designing logistics platform business models, which can provide guidance and assistance for different logistics platforms, are proposed. Second, the design process for logistics platform business models should be dynamically adjusted in real time according to changes and environmental uncertainty. Third, in the process of transitioning to an ecological platform, logistics platforms’ ecosystem service clusters and ecosystem envelope are key factors in achieving a win–win scenario for all the stakeholders in the community. The case studies show that in logistics platform business model design, methods and key steps based on value co-creation could enhance the core competitiveness of logistics platforms. Full article

Understanding the carbon emissions of multimodal travel—comprising walking, metro, bus, cycling, and ride-hailing—is essential for promoting sustainable urban mobility. However, most existing studies focus on single-mode travel, while underlying spatiotemporal and behavioral determinants remain insufficiently explored due to the lack of fine-grained data and interpretable analytical frameworks. This study proposes a novel integration of high-frequency, real-world mobility trajectory data with interpretable machine learning to systematically identify the key drivers of carbon emissions at the individual trip level. Firstly, multimodal travel chains are reconstructed using continuous GPS trajectory data collected in Beijing. Secondly, a model based on Calculate Emissions from Road Transport (COPERT) is developed to quantify trip-level CO₂ emissions. Thirdly, four interpretable machine learning models based on gradient boosting—XGBoost, GBDT, LightGBM, and CatBoost—are trained using transportation and built environment features to model the relationship between CO₂ emissions and a set of explanatory variables; finally, Shapley Additive exPlanations (SHAP) and partial dependence plots (PDPs) are used to interpret the model outputs, revealing key determinants and their non-linear interaction effects. The results show that transportation-related features account for 75.1% of the explained variance in emissions, with bus usage being the most influential single factor (contributing 22.6%). Built environment features explain the remaining 24.9%. The PDP analysis reveals that substantial emission reductions occur only when the shares of bus, metro, and cycling surpass threshold levels of approximately 40%, 40%, and 30%, respectively. Additionally, travel carbon emissions are minimized when trip origins and destinations are located within a 10 to 11 km radius of the central business district (CBD). This study advances the field by establishing a scalable, interpretable, and behaviorally grounded framework to assess carbon emissions from multimodal travel, providing actionable insights for low-carbon transport planning and policy design. Full article

Emerging markets face growing pressures to integrate sustainable English business practices while maintaining economic growth, particularly in addressing environmental challenges and achieving carbon neutrality goals. English Financial information extraction becomes crucial for supporting green finance initiatives, Environmental, Social, and Governance (ESG) compliance, and sustainable investment decisions in these markets. This paper presents FinATG, an AI-driven autoregressive framework for extracting sustainability-related English financial information from English texts, specifically designed to support emerging markets in their transition toward sustainable development. The framework addresses the complex challenges of processing ESG reports, green bond disclosures, carbon footprint assessments, and sustainable investment documentation prevalent in emerging economies. FinATG introduces a domain-adaptive span representation method fine-tuned on sustainability-focused English financial corpora, implements constrained decoding mechanisms based on green finance regulations, and integrates FinBERT with autoregressive generation for end-to-end extraction of environmental and governance information. While achieving competitive performance on standard benchmarks, FinATG’s primary contribution lies in its architecture, which prioritizes correctness and compliance for the high-stakes financial domain. Experimental validation demonstrates FinATG’s effectiveness with entity F1 scores of 88.5 and REL F1 scores of 80.2 on standard English datasets, while achieving superior performance (85.7–86.0 entity F1, 73.1–74.0 REL+ F1) on sustainability-focused financial datasets. The framework particularly excels in extracting carbon emission data, green investment relationships, and ESG compliance indicators, achieving average AUC and RGR scores of 0.93 and 0.89 respectively. By automating the extraction of sustainability metrics from complex English financial documents, FinATG supports emerging markets in meeting international ESG standards, facilitating green finance flows, and enhancing transparency in sustainable business practices, ultimately contributing to their sustainable development goals and climate action commitments. Full article

Research concerning green marketing has predominantly focused on consumer behavior. However, aspects such as the extent to which Small and Medium Enterprises (SMEs) embrace green marketing values, their ability to implement the green marketing mix, and the integration of green marketing into their business culture are critically important. This research aims to provide the 4P (product, price, place, and promotion)-focused green marketing literature with a measurement tool to assess how SMEs implement green marketing practices. The study employed a descriptive design and possesses an exploratory nature. Scale development involved two stages: First, analyses were conducted on a pre-test sample of 159 individuals, revealing the initial scale structure. Second, these analyses were repeated on a larger group of 387 participants. The scale was finalized by confirming the consistency of results across both analyses. Statistical Package for the Social Sciences (SPSS) version 24 and Analysis of Moment Structures (AMOS) version 24 were utilized for descriptive statistics and the scale development process. The final validated 12-item scale demonstrates a robust three-factor structure (“Environmental Promotion”, ”Green Packaging”, and ”Green Distribution”), explaining 62.6% of the total variance. The scale exhibits excellent psychometric properties, including high internal consistency (Cronbach’s α = 0.912), strong model fit from Confirmatory Factor Analysis (CFA), and both convergent and discriminant validity, as indicated by an Average Variance Extracted (AVE) value of 0.605. The scale is deemed applicable to larger populations. Full article

This paper discusses the possibilities of using stump wood as a raw material for energy generation. The research was based on an analysis of the state of knowledge, forest field studies, and participatory observations. A formula was sought to optimise the procurement cost of stump wood appropriate to Polish conditions. Conceptualisation was carried out in a selected area of the Notecka Forest in the Wielkopolska region, located in western Poland. A pilot study was designed to test a computational formula to assess the profitability of harvesting wood from stump wood resources for energy generation. The potential of stump wood is estimated to be around half a million cubic metres per year from the Notecka Forest area alone. This resource provides an opportunity for business development in both forestry and the renewable energy sources (RESs) sector, despite the barriers and risks shown in this study. Full article