Search Results (270)

This study introduces a comprehensive framework for understanding sustainable online shopping preferences, validated using survey data collected in Serbia and Slovenia in 2025 (n = 572), thereby enhancing its generalizability. The primary aim of this research is to examine the extent to which specific environmental, social, and economic indicators influence decision-making processes for online purchasing and delivery. A detailed quantitative analysis was conducted using a structured questionnaire that included a wide range of variables related to online shopping behaviors and delivery preferences. The findings indicate that preferences for sustainability are inherently complex and multifaceted, shaped by critical factors such as environmental concerns, social responsibility, trust, skepticism towards sustainability claims, willingness to pay (WTP), and price sensitivity. Demographic variables, particularly gender and age, show consistent links to preferences for environmental considerations and corporate social responsibility (CSR), while income impacts trust-related behaviors and WTP. Furthermore, the analysis distinguishes between two distinct decision-making approaches: a value-driven sustainability cluster represented by EcoIndex, SocialIndex, and WTPIndex, and a cost-minimization strategy focused on price sensitivity (PriceIndex), with trust acting as a related yet separate factor (CredibilityIndex). Overall, this study emphasizes that a range of interconnected dimensions significantly shape sustainable online shopping preferences. The study was conducted in two developing European countries. Additionally, the findings highlight the need to address universal market barriers, such as price sensitivity, information asymmetry, and consumer skepticism. In a business context, they underscore the importance of adopting advanced analytical methods to enhance decision-making and optimize sustainable business strategies. Full article

Green labels on online travel platforms have become an important mechanism for disclosing environmental information and guiding sustainable tourism consumption. However, when a platform simultaneously provides green certification and competes with suppliers through its self-operated business, green label adoption may reshape both market competition and environmental outcomes. This study develops a game-theoretic model of a co-opetitive tourism platform supply chain consisting of a tourism service supplier (TSS) and an online travel platform (OTP). Two label adoption strategies are compared: the TSS’s self-labeling strategy and its adoption of the OTP-certified green label. The results show that, under self-labeling, the OTP can gain a competitive advantage by setting a higher price and greenness level, although this advantage weakens as consumer recognition of the TSS’s self-label increases. Under platform-certified labeling, the OTP raises the common green standard, which intensifies price competition between the two parties. In most cases, adopting the OTP’s green label improves supply chain profits; however, under certain combinations of competition intensity and platform label credibility, it may reduce the profits of both members and increase environmental damage. These findings suggest that platform-led green certification does not necessarily improve environmental performance and should be designed as a governance mechanism rather than a purely marketing instrument. Full article

In this study, the researchers aim to analyse the motivations of active golf tourists in Spain using the AFE-CFA-Cluster methodology. To this end, a survey was conducted in Spain amongst 381 players, both in person and online, who stated that they had undertaken one golf trip at least once in their lives. The survey consisted of three sections of questions: the first concerned preferences regarding both the destination and the golf courses; the second comprised 20 questions relating to motivations, divided into five categories: business opportunities, financial benefits, escape and relaxation, learning and challenge, and social interaction and camaraderie; and, finally, the third section focused on socio-demographic aspects. The results obtained from the exploratory factor analysis, which were subsequently confirmed by confirmatory factor analysis, revealed the composition of four motivational constructs: business opportunity, financial savings, escape and relaxation, and learning and challenge, resulting in a total of five homogeneous groups of golf tourists: experiential golfers, wellness-oriented golfers, multifunctional golfers, low-involvement golfers and learning-oriented golfers. These results may be useful for companies in the sector and marketing managers in defining the various existing segments of golf tourists and applying specific marketing strategies for each. Full article

As data volumes grow and business requirements become increasingly complex, Hybrid Transactional/Analytical Processing (HTAP) technologies, capable of handling both Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) workloads on a single platform, have gained prominence. HTAP databases typically maintain dual data storage formats and dual query engines: one row-oriented for OLTP, and another column-oriented for OLAP. Query plans, known as hybrid plans, can be segmented and pushed down to execute on these different formats. However, existing HTAP solutions still face challenges in optimizing these hybrid plans, struggling to explore the vast space of potential execution strategies effectively. To address these issues, this study introduces a learning-based query optimizer for row–column dual store HTAP database systems, which automatically generates multiple high-quality query optimizer hints (HINTs) to derive candidate plans. To balance plan generation efficiency with plan quality, a lightweight, learning-based algorithm using Monte Carlo Tree Search (MCTS) for generating hybrid access HINTs is proposed. Moreover, a Transformer-based neural network model coupled with a hybrid plan feature representation method is developed to select the candidate execution plan with the lowest predicted execution time. This work focuses on latency-oriented hybrid-plan selection for analytical queries in a row–column dual-store HTAP architecture; the current evaluation does not cover full mixed OLTP/OLAP workload scheduling, transactional interference, or concurrency control, which are left as future work. Experimental results on AlloyDB Omni, a recent row–column dual-store HTAP database, using the real-world IMDB dataset and JOB benchmark demonstrate that our system reduces execution time by 75.02% compared to the Cost-Based Optimizer (CBO) and by 62.23% compared to the state-of-the-art row-store-based learning query optimizer in this evaluated analytical-query setting. Full article

Adopting a circular economy approach requires new business models, multi-stakeholder engagement, and tailored financial models and mechanisms as core pillars. This paper examines the conditions needed to scale circular economy initiatives in Europe by analysing insights collected from the DECISO project and conducting a comparative analysis of 38 European projects. The study adopts a mixed methods approach that integrates an online stakeholder survey with inputs generated through participatory workshops and discussions of selected use cases. This combined approach is used to identify the main structural barriers limiting the maturity and investment readiness of circular economy projects, such as regulatory complexity, difficulties in accessing funding, and weak stakeholder dialogue mechanisms. The approach was also used for enabling factors that can support development of circular economy. Particular attention is given to the role of project development assistance, modular financing strategies, and de-risking tools, which are highlighted as crucial elements for supporting the technical and economic credibility of projects and attracting public and private investors. The article also identifies and addresses seven unresolved research gaps in the literature, including the lack of interoperable policy instruments, the absence of business models capable of integrating investor expectations, the paucity of integrated methodologies for assessing technical and economic regulatory feasibility, and the need for trust-building procedures. The findings suggest that the transition to a regenerative economy requires a systemic approach based on coherent policies, de-risking financial instruments, collaborative governance, and strategic technical support throughout the project development cycle. Full article

Systems that farm non-typical (wild) species for human consumption are on the rise globally, in contrast to more typical livestock production. In some instances, wildlife farming may arguably help alleviate poverty, provide sustainable animal protein, and be a useful strategy for conservation through reducing wildlife poaching or breeding some animals on farms for reintroduction. However, it is unclear whether farming non-typical species within variable and often unregulated systems truly offers these benefits or outweighs the costs including animal welfare implications, public health concerns, and normalising or intensifying the consumption of wild animals. A previous study proposed a sentiocentric ethical decision-making framework for the farming of wild species. In the present study we invited academic ‘key informants’ with specialised knowledge about farming non-typical species to pilot the framework via an online survey using a species of their choice and requested their feedback on its strengths and weaknesses. Thirteen respondents applied ten different mammalian, reptilian, insect, and avian species to the framework, spanning all continents. Ultimately, the framework outcome for 11 appraisals was that the chosen species may be suitable for farming. However, erroneous responses were likely in places, and there was some uncertainty over definitions of framework terminology. We publish resultant amendments to the ethical framework to clarify meaning and suggest that it can be applied proactively or reactively by different stakeholders (e.g., governments, businesses, and NGOs). We reflect our informants’ views, acknowledging the need to solicit expertise from additional stakeholders (e.g., farmers) and the role of cultural significance and rural communities when considering farming non-typical species. Full article

This study develops a comprehensive carbon footprint assessment model that integrates forward and reverse logistics to evaluate and compare greenhouse gas emissions from online and offline apparel sales channels in China, with a particular focus on high return rates. The model quantifies emissions from transportation, packaging, storage, and operations, incorporating return and exchange logistics. The system boundary is limited to enterprise-controllable sales-phase activities and excludes consumer travel. Three sales models are compared: factory-to-consumer (F2C), traditional business-to-consumer (B2C) e-commerce, and brick-and-mortar retail (BMR). Within this defined boundary, BMR exhibits the lowest carbon footprint (0.296 kg CO₂e/item), followed by F2C (0.408 kg CO₂e/item) and B2C (0.602 kg CO₂e/item). Packaging dominates online emissions (55–57%), whereas store operations are the main contributor to offline emissions (43%). Return rates are identified as a decisive factor, accounting for over 31% of e-commerce emissions and potentially increasing them by 171.3% under extreme scenarios. Sensitivity analysis reveals that trunk line distance (factory to warehouse) has a greater impact on emissions than last-mile return route optimization. Relocating the factory closer to consumers reduces B2C transport emissions by 72.3%, whereas replacing conventional packaging with recycled plastic reduces total B2C emissions by 46.0%. These findings provide channel-specific sustainability strategies: return reduction and packaging innovation for online channels, and energy efficiency improvements for physical stores. These results are conditional on the defined system boundary. If consumer travel by private car were included, the relative advantage of offline channels would diminish or could reverse. Full article

Delivery services have drawn much attention and become of topmost significance in urban areas by presenting online food delivery selections for a diversity of dishes from a wide range of restaurants, decreasing both travel and waiting times. Customer data analysis acts as a cornerstone in corporate strategy, allowing enterprises to gather and interpret user feedback and helping them to make informed decisions that drive future business development. However, major knowledge gaps remain due to the scarcity of literature review studies on these delivery services, hindering a complete understanding of customer satisfaction in this sector. Furthermore, there has been little systematic research on managerial response tactics to online consumer complaints and negative reviews. Researchers have contributed by applying artificial intelligence, including deep learning and machine learning models, to analyze customer sentiment and understand customer brand perceptions. This study presents a Hybrid Deep Neural Network Model for Customer Satisfaction Analysis (HDNNM-CSA), with the aim of developing an efficient model which is capable of accurately classifying customer satisfaction levels in delivery apps based on textual responses provided by customer experience managers. To achieve this, the model initially pre-processes text data using text cleaning, emoji removal, normalization, tokenization, stop word removal, and stemming to clean and standardize the unstructured text data for further analysis. Following this, term frequency–inverse document frequency-based word embedding is utilized to transform the pre-processed text into meaningful feature representations. Lastly, an ensemble architecture involving bidirectional long short-term memory, temporal convolutional, and graph convolutional networks is deployed to classify customer satisfaction levels with managers’ responses. A series of experimental analyses are performed, and the results are examined for numerous features. A comparative analysis demonstrates the enhanced performance of the HDNNM-CSA technique with respect to existing approaches. Full article

Artificial intelligence (AI) systems and tools are increasingly reshaping educational practices. This study examines perspectives shared in student-focused online communities on AI’s impact on education, comparing those of computer science (CS) and business students through an analysis of Reddit posts. Using natural language processing (NLP), sentiment analysis, and Latent Dirichlet Allocation (LDA) topic modeling, we analyzed 1108 posts collected from six subreddits. Results reveal distinct thematic focuses: CS students emphasize technical aspects, including programming efficiency, coding assistance, and concerns about job displacement, while business students focus on decision-making enhancement, financial analysis applications, and operational efficiency. Sentiment analysis indicates that the Business/Finance-oriented corpus is slightly more positive than the CS-oriented corpus (51.9% vs. 50.1% positive). The CS-oriented corpus also contains a higher proportion of negative posts (36.0% vs. 33.2%). These differences reflect discipline-specific epistemological frameworks shaping AI perception. The findings provide educators with guidelines for developing tailored AI integration strategies that address discipline-specific concerns and opportunities. This study contributes to understanding how academic background influences perceptions of AI in education, offering insights for curriculum design and policy development. Full article

Background/Objectives: Nonpharmaceutical interventions such as social distancing and face mask use were central to controlling infectious disease transmission during the early phases of the COVID-19 pandemic, particularly when vaccines and treatments were limited or unevenly available. Although public health strategies emphasized individual compliance, adherence varied widely. Empirical evidence remains limited regarding how individuals integrate influences across individual, interpersonal, and community levels when assessing their ability and readiness to socially distance. This study examined how residents evaluated, prioritized, and experienced multi-level factors shaping perceived ability and readiness to practice social distancing during the early phase of the COVID-19 epidemic. Methods: We conducted a cross-sectional online survey of adults (≥18 years) residing in St. Louis City and St. Louis County, Missouri, between April and July 2020. Participants selected and ranked individual/interpersonal and community-level factors influencing social distancing and provided open-ended explanations of their choices. Quantitative data were analyzed descriptively to assess selection frequency and ranking priority. Qualitative responses were analyzed using iterative thematic coding to examine how participants interpreted and combined these factors. Results: The analytic sample included 1692 respondents. At the individual/interpersonal level, family and friends’ distancing behavior (58.9%), desire for in-person interaction (52.4%), and personal risk of COVID-19 (48.9%) were frequently selected, while personal risk, caring for others, and ability to work from home were most often ranked as the highest priority. At the community level, others’ distancing in public spaces (66.2%), availability of COVID-19 testing (58.9%), and businesses’ ability to ensure distancing and sanitation (57.2%) were most frequently selected, with epidemic severity, testing availability, and treatment availability ranked as most influential. Qualitative findings indicated that respondents experienced these influences as interconnected, integrating personal and relational risk, local epidemic conditions, healthcare access, visible community norms, and employer policies. Conclusions: Perceived ability and readiness to practice social distancing emerge from interdependent social and structural conditions rather than isolated individual motivations. Public health responses to emerging infectious diseases may be more effective when individual-level guidance is complemented by accessible testing and treatment, supportive workplace policies, and community environments that visibly reinforce protective behaviors. Full article

Artificial intelligence (AI) is rapidly transforming language education. However, its pedagogical and sociocultural impacts on Japanese business writing remain underexplored. This study aims to examine how ChatGPT4o-based automated feedback functions within Japanese business writing education for adult learners in online higher education, with particular attention to both its instructional impact and learners’ sociocultural awareness. Situated in a cyber university context where the proportion of adult learners is increasing, the study explores the potential of AI-mediated feedback to address learners’ diverse educational and cultural needs. It employed a mixed-methods design, combining a survey of 27 participants and in-depth interviews with 11 participants. The interviews were transcribed and thematically coded to gain deeper insights into learners’ perceptions. The findings indicate that ChatGPT feedback contributed to learners’ planning of study strategies, the provision of immediate and personalized corrections, the reinforcement of error awareness, and the acquisition of honorific and polite expressions. On the one hand, learners reported that they could quickly understand regional language practices and communication conventions in business contexts, thereby deepening their cultural sensitivity. On the other hand, some learners expressed concern that increased reliance on AI could weaken exploratory and critical learning. These results suggest that ChatGPT can serve not merely as a correction tool but also as an educational resource that simultaneously fosters self-directed learning and sociocultural competence. However, to ensure reliability and cultural appropriateness, hybrid feedback incorporating instructor guidance is necessary. This study has academic significance in demonstrating the potential of extending AI-based feedback to Japanese business communication education, thereby constructing an integrated language and culture learning environment. Full article

This study addresses a pivotal strategic issue in hospitality e-commerce: how hotels can optimize cooperation with heterogeneous online travel agencies (OTAs). Moving beyond the conventional question of whether to cooperate, we investigate the interrelated decisions of which OTA type to partner with (quality-focused vs. price-focused) and which business model to adopt (merchant vs. agency). We develop a game-theoretic model that incorporates key e-commerce factors, including hotel capacity constraints, cross-channel spillover effects, and differential consumer acceptance of OTA types. Our analysis yields a contingent decision framework. We demonstrate that OTA cooperation becomes beneficial only when a hotel’s room capacity exceeds its direct-channel demand. The optimal strategy evolves with capacity: hotels with moderate capacity should partner with a single OTA type—predominantly the quality-focused one—while larger hotels should engage both types to maximize market coverage. In terms of business models, smaller hotels benefit from the risk-shifting merchant model, whereas larger hotels capture higher margins through the agency model. A key finding is the general superiority of a differentiated approach: applying the agency model to quality-focused OTAs and the merchant model to price-focused OTAs. This research provides a structured analytical framework to guide hotel managers in crafting e-commerce platform strategies and offers scholars a foundation for further inquiry into platform competition and contract design in digital marketplaces. Full article

In the rapidly evolving digital economy, the expansion of business-to-business e-commerce ecosystems has compelled traditional industries to integrate into digital supply chains to achieve sustainable development. Industrial e-commerce is no longer limited to online transactions but extends to the digital transformation of backend operations. Drawing upon the perspective of the digital business ecosystem, this study investigates how digital supply chain integration, manifested through digital transformation, impacts energy efficiency. By utilizing a panel fixed effects model and advanced text mining techniques on a dataset of 721 listed firms in the resource-intensive sectors of China spanning from 2011 to 2023, this research constructs a novel index to quantify corporate digital maturity based on semantic analysis. The empirical results demonstrate that digital transformation significantly enhances energy efficiency by facilitating optimized resource allocation and data-driven decision making required by modern digital markets. Mechanism analysis reveals that green innovation functions as a pivotal mediator that bridges the gap between digital investments and environmental performance. Furthermore, this relationship is found to be contingent upon corporate social responsibility strategies, ownership structures, and the scale of the firm. This study contributes to the electronic commerce literature by elucidating how traditional manufacturers can leverage digital technologies and green innovation to navigate the twin transition of digitalization and sustainability, offering theoretical implications for platform governance in industrial sectors. Full article

The growing global environmental crisis calls for fundamental transformations in production and consumption systems, but the understanding of how circular economy strategies translate into quantifiable environmental benefits remains fragmented across sectors and geographies. The objective of this study is to synthesize current scientific knowledge on the circular economy as an environmental mitigation strategy, identifying conceptual convergences, methodological patterns, geographic distributions, and critical knowledge gaps. A systematic review combined with a bibliometric analysis of 62 peer-reviewed articles published between 2018 and 2024, retrieved from Scopus, Web of Science, ScienceDirect, Springer Link and Wiley Online Library, was conducted following the PRISMA 2020 guidelines. The results reveal a marked methodological convergence around life cycle assessment, with Europe dominating the scientific output (58% of the corpus). Four complementary conceptual frameworks emerged, emphasizing closed-loop material flows, environmental performance, integration of economic sustainability and business model innovation. The thematic analysis identified bioenergy and waste valorization as the most mature implementation pathways, constituting 23% of the research emphasis. However, critical gaps remain: geographic concentration limits the transferability of knowledge to diverse socioeconomic contexts; social, cultural and behavioral dimensions remain underexplored (12% of publications); and environmental justice considerations receive negligible attention. Crucially, the evidence reveals nonlinear relationships between circularity metrics and environmental outcomes, calling into question automatic benefits assumptions. This review contributes to an integrative synthesis that advances theoretical understanding of circularity-environment relationships while providing evidence-based guidance for researchers, practitioners, and policy makers involved in transitions to the circular economy. Full article

Country parks are an important instrument for implementing China’s strategies on ecological civilization and integrated urban–rural development. This study conducted field surveys in seven country parks of Shanghai. Meanwhile, stakeholder seminars were organized with local residents and park authorities. To assess visitor satisfaction, a questionnaire survey was administered both on-site and online. Through case analysis and a policy review, this study systematically identifies key challenges in leveraging country parks for rural revitalization. The findings indicate that visitors highly value the ecological qualities of the parks, and basic infrastructure like roads and resting facilities generally meets expectations. However, shuttle services and smart guiding systems remain notable shortcomings that hinder the overall visitor experience. Moreover, gaps in service quality, local cultural representation, and the depth of nature education constitute the primary weaknesses affecting visitor satisfaction. Regarding rural revitalization, this study identifies four main limitations in the contribution of country parks: (1) Inadequate functional positioning and weak integration with surrounding resources; (2) Low land use efficiency and an unbalanced provision of supporting facilities; (3) Homogenized industrial formats with limited innovation and integration capacity; and (4) Restricted participation of local farmers and underdeveloped multi-stakeholder governance mechanisms. To address these issues, this study proposes four strategic recommendations: (1) Develop distinctive local brands and strengthen synergies with surrounding resources; (2) Promote mixed land use and enhance supporting service facilities; (3) Foster diversified business formats and facilitate the value realization of ecological products; and (4) Expand income-generation channels for farmers and improve multi-stakeholder governance frameworks. The research demonstrates that optimizing the functions of country parks can improve ecological and recreational services and help establish an integrated “ecology–industry–community” framework through industrial chain extension and community participation, thereby supporting rural revitalization. Full article