Search Results (268)

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

Growing awareness of sustainable development and green consumer concerns is driving the market expansion for green agriculture products. E-commerce live streaming gives rural enterprises a new channel through scenario-building and interaction, while agro-tourism integration combines resources to generate a variety of promotion scenarios. This study examines the effects of external stimuli, including social networks, resource endowment, infrastructure, and the characteristics of e-commerce streamers, on the perception, trust, perceived value, and purchase intention of green consumption. It is based on the SOR (Stimulus–Organism–Response) theoretical model and focuses on e-commerce live streaming in the agriculture-tourism integration scenario. According to a structural equation modeling (SEM) analysis of 350 consumer questionnaires, these external stimuli primarily influence purchase intention through perceived value, trust, and green consumption cognition, with resource endowment having the most significant impact. The effects of infrastructure on perceived value and streamer attractiveness on green consumption cognition are not statistically significant. This research not only broadens the use of the SOR model in the emerging field of agritourism integration but also offers rural businesses theoretical backing and useful guidance to maximize e-commerce live marketing and enhance agritourism integration. Full article

Corporate environmental claims often neglect the substantial ecological impact of land-use changes. This case study examines the spatial dimension of retail-driven land-use transformation by analyzing supermarket expansion in the Veneto region (northern Italy), with a focus on a large grocery retailer. We evaluated its corporate environmental claims by assessing land consumption patterns from 1983 to 2024 using Geographic Information Systems (GIS). The GIS-based methodology involved geocoding 113 Points of Sale (POS—individual retail outlets), performing photo-interpretation of historical aerial imagery, and classifying land-cover types prior to construction. We applied spatial metrics such as total converted surface area, land-cover class frequency across eight categories (e.g., agricultural, herbaceous, arboreal), and the average linear distance between afforestation sites and POS developed on previously rural land. Our findings reveal that 65.97% of the total land converted for Points of Sale development occurred in rural areas, primarily agricultural and herbaceous lands. These landscapes play a critical role in supporting urban biodiversity and providing essential ecosystem services, which are increasingly threatened by unchecked land conversion. While the corporate sustainability reports and marketing strategies emphasize afforestation efforts under their “We Love Nature” initiative, our spatial analysis uncovers no evidence of actual land-use conversion. Additionally, reforestation activities are located an average of 40.75 km from converted sites, undermining their role as effective compensatory measures. These findings raise concerns about selective disclosure and greenwashing, driving the need for more comprehensive and transparent corporate sustainability reporting. The study argues for stronger policy frameworks to incentivize urban regeneration over greenfield development and calls for the integration of land-use data into corporate sustainability disclosures. By combining geospatial methods with content analysis, the research offers new insights into the intersection of land use, business practices, and environmental sustainability in climate-vulnerable regions. Full article

As Chinese firms play pivotal roles in global supply chains, multinational corporations face increasing pressure to ensure ESG accountability across their sourcing networks. Current ESG rating systems lack transparency in incorporating China’s unique industrial, economic, and cultural factors, creating reliability concerns for stakeholders managing supply chain sustainability risks. This study develops an explainable artificial intelligence framework using SHAP and permutation feature importance (PFI) methods to predict the ESG performance of Chinese firms. We analyze comprehensive ESG data of 1608 Chinese listed companies over 13 years (2009–2021), integrating financial and non-financial determinants traditionally examined in isolation. Empirical findings demonstrate that random forest algorithms significantly outperform multivariate linear regression in capturing nonlinear ESG relationships. Key non-financial determinants include patent portfolios, CSR training initiatives, pollutant emissions, and charitable donations, while financial factors such as current assets and gearing ratios prove influential. Sectoral analysis reveals that manufacturing firms are evaluated through pollutant emissions and technical capabilities, whereas non-manufacturing firms are assessed on business taxes and intangible assets. These insights provide essential tools for multinational corporations to anticipate supply chain sustainability conditions. Full article

Sustainable development has emerged as a critical priority for the global community, influencing all aspects of development worldwide. Within this context, the role of education and training in advancing sustainable development can contribute to this. This research aims to explore whether the integration of Information and Communication Technology educational tools into the curricula of engineering technicians helps trainees better understand the concepts of climate change and resource management, which are directly linked to the green transition and the green economy, compared to traditional educational methods. The study was conducted with trainees from Higher Vocational Training Schools (SAEKs) in the wider Athens area, Greece. According to the results, using educational technology to teach engineering courses aids students in developing the competencies needed to change production processes and business models in the direction of a greener future. This is especially crucial as future technicians will be able to use cutting-edge methods to lower emissions and boost resource use efficiency. The findings of the study could provide important information for all those involved in the design of educational curricula of engineering technicians. Concerns and thoughts on the effective use of educational technology in the educational process are also expressed. Full article

This study analyses aspects related to the electromobility transition. Emerging technologies have enabled the production and commercialisation of electric vehicles to reduce polluting emissions. However, significant obstacles are present in this global transition. The analysis identifies that public policies play a crucial role in the development of electromobility, and emphasises how new business models in electromobility are emerging to satisfy changing customer demands. Concerns related to raw materials extraction, battery disposal, and vehicle-to-grid (V2G) integration are also important to consider. The relationship between technologically advanced countries and raw material-producing nations must balance socioeconomic, historical, labour, and ecological factors. In order to have a standard reference, this study considers for the analysis the political, economic, social, technological, environmental, and legal factors (PESTLE). An analysis of future scenarios considering pessimistic and optimistic trends revealed that, compared with the actual trends, important actions must be taken to develop electromobility not only from the technological aspect. These results provide a comprehensive analysis of electromobility sustainability and its importance for multidisciplinary stakeholders related to the actual challenges towards electromobility, the electric network capabilities, and the importance of creating new jobs and products based on a circular and sustainable economy. Full article

Amid growing concern about labour market resilience in an era of digital and green transitions, this study carries out an investigation on how academic innovation and industrial transformation jointly shape sustainable employment outcomes across EU-27 member states. We frame this inquiry within the emerging concept of the “employment trilemma”, which posits inherent tension between competitiveness, innovation, and social inclusiveness in modern economies. Drawing on a dynamic panel dataset (2005–2023) and employing System SMM estimations, we test the hypothesis that the alignment of academic innovation systems and industrial transformation strategies enhances long-term employment sustainability. Our results reveal a nuanced relationship: academic innovation significantly supports employment in countries with high knowledge absorption capacity, whereas industrial transformation contributes positively only when embedded in cohesive, inclusive economic frameworks. Thus, these findings provide valuable insights for international business due to their emphasis on the importance of cross-sectoral collaboration, policy synchronisation, and investment in human capital for firms navigating increasingly volatile labour markets. Likewise, the study offers actionable insights for business leaders, policymakers, and universities striving to balance innovation with equitable labour market outcomes in an integrated European economy. Full article

The increasing integration of sensing devices with smart technologies, deep learning algorithms, and robotics is profoundly transforming the agricultural sector in the context of Farming 4.0. These technological advancements constitute critical enablers for the development of customized, data-driven farming systems, offering potential solutions to the challenges of agricultural intensification while addressing societal concerns associated with the emerging paradigm of “farming by numbers”. The Precision Livestock Farming (PLF) systems enable the continuous, real-time, and individual sensing of livestock in order to detect subtle change in animals’ status and permit timely corrective actions. In addition, smart technology implementation within the housing environment leads the whole farming sector towards enhanced business rentability and food security as well as increased animal health and welfare conditions. Looking to the future, the collection, processing, and analysis of data with advanced statistic methods provide valuable information useful to design predictive models and foster the insight on animal welfare, environmental sustainability, farming productivity, and profitability. This review highlights the significant potential of implementing advanced sensing systems in livestock farming, examining the scientific foundations of PLF and analyzing the main technological applications driving the transition from traditional practices to more modern and efficient farming models. Full article

CCS/CCUS is considered vital for global climate mitigation, especially in decarbonizing hard-to-abate sectors like upstream oil and gas. In Indonesia, however, its deployment remains limited due to fragmented stakeholder views and lack of integrated policy support. This study explores multi-stakeholder perspectives, including government, academia, business, finance, media, and civil society, on the role and feasibility of CCS/CCUS in achieving the country’s net zero emissions (NZE) target. Using a mixed-method approach, we conducted structured surveys (n = 39) and in-depth interviews (n = 34). Findings reveal broad support for CCS/CCUS but highlight ongoing concerns about economic viability, regulatory uncertainty, and environmental risks. Stakeholders emphasize the need for stronger government incentives and cross-border financing mechanisms. The study underscores the importance of inclusive policymaking, enhanced fiscal support, and integration of CCS/CCUS into Indonesia’s carbon economic value framework to ensure a more participatory and sustainable climate policy pathway. Full article

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) in Building Energy Management Systems (BEMSs) offers transformative potential for improving energy efficiency, enhancing occupant comfort, and supporting grid stability. However, the adoption of these technologies in the European Union (EU) is significantly influenced by a complex regulatory landscape, including the EU AI Act, the General Data Protection Regulation (GDPR), the EU Cybersecurity Act, and the Energy Performance of Buildings Directive (EPBD). This review systematically examines the legal, technological, and economic implications of these regulations on AI- and IoT-driven BEMS. Following the PRISMA-ScR guidelines, 64 relevant sources were reviewed, comprising 34 peer-reviewed articles and 30 regulatory or policy documents. First, legal and regulatory barriers that may hinder innovation are identified, including data protection constraints, cybersecurity compliance, liability concerns, and interoperability requirements. Second, technological challenges in designing regulatory-compliant AI and IoT solutions are examined, with a focus on data privacy-preserving architectures (e.g., edge computing versus cloud processing), explainability requirements for AI decision-making, and cybersecurity resilience. Finally, the economic opportunities arising from regulatory alignment are highlighted, demonstrating how compliant AI and IoT-based BEMS can enable energy savings, operational efficiencies, and new business models in smart buildings. By synthesizing current research and policy developments, this review offers a comprehensive framework for understanding the intersection of regulatory requirements and technological innovation in AI-driven building management. Strategies are discussed for navigating regulatory constraints while leveraging AI and IoT for energy-efficient, intelligent building operations. The insights presented aim to support researchers, policymakers, and industry stakeholders in advancing regulatory-compliant BEMS that balance innovation, security, and sustainability. Full article

This study aims to identify and evaluate the essential design features, strengths, and limitations of a virtual reality (VR) application that has been developed to train an international sales force effectively for a premium global wine brand. The study emphasizes the value of stakeholder-driven iterative development and systematic evaluations. A case study methodology was adopted for the research, focusing on a VR training application, developed for Marchesi Antinori. The Scrum framework was employed to facilitate iterative stakeholder collaboration. A qualitative evaluation was conducted using focus groups, comprising marketing, communications, and sales representatives. A systematic application of natural language processing (NLP) embedding techniques and recursive clustering analyses was undertaken to interpret stakeholder feedback. The findings suggest that stakeholder-driven, iterative processes can significantly enhance the effectiveness of VR applications by providing a clear structure for immersive storytelling that focuses on terroir characteristics, vineyard operations, and cellar practices. Stakeholders acknowledged the potent educational benefits of VR in regard to business-to-business (B2B) sales training. However, they also highlighted significant limitations, including user discomfort, concerns about authenticity, and variations in market receptivity. Alternative immersive technologies, including augmented reality and immersive multimedia environments, have emerged as valuable complementary approaches. This study addresses a significant gap in the literature by examining the application of VR technology for B2B sales training in the premium wine industry. The study integrates an iterative Scrum methodology with advanced natural language processing (NLP) analytical techniques to derive nuanced, context-rich insights. Full article

While digital transformation in e-commerce receives the most publicity, applications in energy and water utilities have been ongoing for decades. Using a methodology based on a systematic review, the paper offers a model of how it occurs in water utilities, reviews experiences from the field, and derives lessons learned to create a road map for future research and implementation. Innovation in water utilities occurs more in the field than through organized research, and utilities share their experiences globally through networks such as water associations, focus groups, and media outlets. Their digital transformation journeys are evident in business practices, operations, and asset management, including methods like decision support systems, SCADA systems, digital twins, and process optimization. Meanwhile, they operate traditional regulated services while being challenged by issues like aging infrastructure and workforce capacity. They operate complex and expensive distribution systems that require grafting of new controls onto older systems with vulnerable components. Digital transformation in utilities is driven by return on investment and regulatory and workforce constraints and leads to cautious adoption of innovative methods unless required by external pressures. Utility adoption occurs gradually as digital tools help utilities to leverage system data for maintenance management, system renewal, and water loss control. Digital twins offer the advantages of enterprise data, decision support, and simulation models and can support distribution system optimization by integrating advanced metering infrastructure devices and water loss control through more granular pressure control. Models to anticipate water main breaks can also be included. With such advances, concerns about cyber security will grow. The lessons learned from the review indicate that research and development for new digital tools will continue, but utility adoption will continue to evolve slowly, even as many utilities globally are too stressed with difficult issues to adopt them. Rather than rely on government and academics for research support, utilities will need help from their support community of regulators, consultants, vendors, and all researchers to navigate the pathways that lie ahead. Full article

Early and accurate detection of breast cancer is essential for reducing mortality rates and improving clinical outcomes. However, deep learning (DL) models used in healthcare face significant challenges, including concerns about data privacy, domain-specific overfitting, and limited interpretability. To address these issues, we propose BreastSwinFedNetX, a federated learning (FL)-enabled ensemble system that combines four hierarchical variants of the Swin Transformer (Tiny, Small, Base, and Large) with a Random Forest (RF) meta-learner. By utilizing FL, our approach ensures collaborative model training across decentralized and institution-specific datasets while preserving data locality and preventing raw patient data exposure. The model exhibits strong generalization and performs exceptionally well across five benchmark datasets—BreakHis, BUSI, INbreast, CBIS-DDSM, and a Combined dataset—achieving an F1 score of 99.34% on BreakHis, a PR AUC of 98.89% on INbreast, and a Matthews Correlation Coefficient (MCC) of 99.61% on the Combined dataset. To enhance transparency and clinical adoption, we incorporate explainable AI (XAI) through Grad-CAM, which highlights class-discriminative features. Additionally, we deploy the model in a real-time web application that supports uncertainty-aware predictions and clinician interaction and ensures compliance with GDPR and HIPAA through secure federated deployment. Extensive ablation studies and paired statistical analyses further confirm the significance and robustness of each architectural component. By integrating transformer-based architectures, secure collaborative training, and explainable outputs, BreastSwinFedNetX provides a scalable and trustworthy AI solution for real-world breast cancer diagnostics. Full article

This paper explores the role of export-oriented firms in shaping regional economic development, with a focus on their operational footprint, strategic orientation, and interaction with institutional and infrastructural environments. Set within the broader context of regional competitiveness and sustainable growth, the study examines how firms in geographically peripheral and structurally challenged areas position themselves within global markets. Emphasis is placed on understanding the internal and external factors that influence export performance, innovation capacity, and the integration of sustainability principles into business practices. The research adopts a survey-based methodology, collecting data from firms located in a cross-border region to assess their perceptions of trade barriers, infrastructure needs, strategic values, and environmental awareness. The analysis draws on established frameworks in regional development, international business, and sustainability transitions, offering a multidimensional perspective on firm behavior. By linking firm-level insights with regional development policy, the study contributes to ongoing discussions around how enterprises in remote regions can overcome structural constraints and engage more fully with global value chains. It also supports the growing call for place-based, context-sensitive strategies that align economic competitiveness with innovation, digital transformation, and environmental responsibility. This integrated approach offers valuable implications for both policymakers and practitioners concerned with fostering inclusive and resilient regional economies. Full article

Online marketing environments are rapidly being transformed by Artificial Intelligence (AI). This represents the implementation of Machine Learning (ML) that has significant potential in content personalization, enhanced usability, and hyper-targeted marketing, and it will reconfigure how businesses reach and serve customers. This systematic examination of machine learning in the Digital Marketing (DM) industry is also closely examined, focusing on its effect on human–computer interaction (HCI). This research methodically elucidates how machine learning can be applied to the automation of strategies for user engagement that increase user experience (UX) and customer retention, and how to optimize recommendations from consumer behavior. The objective of the present study is to critically analyze the functional and ethical considerations of ML integration in DM and to evaluate its implications on data-driven personalization. Through selected case studies, the investigation also provides empirical evidence of the implications of ML applications on UX/customer loyalty as well as associated ethical aspects. These include algorithmic bias, concerns about the privacy of the data, and the need for greater transparency of ML-based decision-making processes. This research also contributes to the field by delivering actionable, data-driven strategies for marketing professionals and offering them frameworks to deal with the evolving responsibilities and tasks that accompany the introduction of ML technologies into DM. Full article