Search Results (202)

The integration of artificial intelligence (AI), big data analytics, and blockchain technologies within the digital economy presents transformative opportunities for promoting low-carbon urban development. However, a systematic understanding of how these digital innovations influence urban carbon mitigation remains limited. This study addresses this gap by proposing two research questions (RQs): (1) What are the key success factors for artificial intelligence, big data, and blockchain in urban carbon emission reduction? (2) How do these technologies interact and support the transition to low-carbon cities? To answer these questions, the study employs a hybrid methodological framework combining the decision-making trial and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM) techniques. The data were collected through structured expert questionnaires, enabling the identification and hierarchical analysis of twelve critical success factors (CSFs). Grounded in sustainability transitions theory and institutional theory, the CSFs are categorized into three dimensions: (1) digital infrastructure and technological applications; (2) digital transformation of industry and economy; (3) sustainable urban governance. The results reveal that e-commerce and sustainable logistics, the adoption of the circular economy, and cross-sector collaboration are the most influential drivers of digital-enabled decarbonization, while foundational elements such as smart energy systems and digital infrastructure act as key enablers. The DEMATEL-ISM approach facilitates a system-level understanding of the causal relationships and strategic priorities among the CSFs, offering actionable insights for urban planners, policymakers, and stakeholders committed to sustainable digital transformation and carbon neutrality. Full article

Although product environmental information serves as an effective tool for promoting green consumption which is a critical lever for advancing broader sustainability goals, its varied impacts across product categories (durable goods vs. fast-moving consumer goods) and the underlying mechanisms remain unexplored. Grounded in the theory of consumption values (TCV), this study investigated the heterogeneous effects and mediating pathways of such information through a comparative analysis of representative products: organic milk (fast-moving consumer goods, FMCGs) and energy-efficient air conditioners (durable goods). The results show the following: (1) epistemic value, which exhibits the strongest association with product environmental information, demonstrates significantly different influence patterns between purchases of green durable goods and green FMCGs across both online and offline channels; (2) in the e-commerce context, green FMCG consumption is mainly driven by product environmental information through the mediating effect of conditional value. For green durable goods, product environmental information influences green consumption through multiple pathways including functional value, conditional value, and epistemic value. This study extends the classic theory of consumption values, and the results suggest that differentiated information strategies of emphasizing conditional value for FMCGs and integrating multi-dimensional values for durables can optimize green consumption promotion. Such strategies hold substantial potential to strengthen the green development of the omnichannel retailing sector, reinforcing its contribution to reaching sustainability objectives. Full article

Background: Type 2 diabetes (T2D) is a major global health concern influenced by sociodemographic and lifestyle factors. This study compared T2D risk scores between commerce and industry sectors and assessed the associations of age, sex, education, physical activity, diet, and smoking with elevated risk. Methods: This cross-sectional study included 56,856 men and 12,872 women employed in the commerce (n = 27,448) and industry (n = 42,280) sectors across Spain. Anthropometric, clinical, and biochemical data were collected. Four validated T2D risk scores (QDscore, Finrisk, Canrisk, and TRAQ-D) were calculated. Multinomial logistic regression models estimated adjusted odds ratios (ORs) for high-risk categories by sociodemographic and lifestyle characteristics. Results: Women in the industrial sector had significantly higher age, BMI, waist circumference, and lipid levels than those in commerce; differences among men were less marked. Across all participants, higher T2D risk scores were independently associated with physical inactivity (OR up to 12.49), poor Mediterranean diet adherence (OR up to 6.62), industrial employment (OR up to 1.98), and older age. Male sex was strongly associated with high Canrisk scores (OR = 6.31; 95% CI: 5.12–7.51). Conclusions: Employment in the industrial sector, combined with sedentary behavior and poor dietary habits, is independently associated with higher predicted T2D risk. Workplace prevention strategies should prioritize multicomponent interventions targeting modifiable risk factors, especially in high-risk subgroups such as older, less-educated, and inactive workers. Full article

In the realm of recommender systems, the prediction of diverse customer preferences has emerged as a compelling research challenge, particularly for multi-state business organizations operating across various geographical regions. Collaborative filtering, a widely utilized recommendation technique, has demonstrated its efficacy in sectors such as e-commerce, tourism, hotel management, and entertainment-based customer services. In the item-based collaborative filtering approach, users’ evaluations of purchased items are considered uniformly, without assigning weight to the participatory data sources and users’ ratings. This approach results in the ‘relevance problem’ when assessing the generated recommendations. In such scenarios, filtering collaborative patterns based on regional and local characteristics, while emphasizing the significance of branches and user ratings, could enhance the accuracy of recommendations. This paper introduces a turnover-based weighting model utilizing a big data processing framework to mine multi-level collaborative filtering patterns. The proposed weighting model assigns weights to participatory data sources based on the turnover cost of the branches, where turnover refers to the revenue generated through total business transactions conducted by the branch. Furthermore, the proposed big data framework eliminates the forced integration of branch data into a centralized repository and avoids the complexities associated with data movement. To validate the proposed work, experimental studies were conducted using a benchmarking dataset, namely the ‘Movie Lens Dataset’. The proposed approach uncovers multi-level collaborative pattern bases, including global, sub-global, and local levels, with improved predicted ratings compared with results generated by traditional recommender systems. The findings of the proposed approach would be highly beneficial to the strategic management of an interstate business organization, enabling them to leverage regional implications from user preferences. Full article

Determining the optimal portfolio is important in the investment process because it includes the selection of appropriate fund allocation to manage financial risk effectively. Although risk cannot be entirely eliminated, it is managed through strategic allocation based on investor preferences. Therefore, this research aimed to use mathematical models, including the power utility function, the natural logarithm utility function, and a combination of both, to capture varying degrees of risk aversion. The optimal allocation was obtained by analytically maximizing the expected end-of-period wealth utility under each specification, where the investor level of risk aversion was derived by determining the constant. The utility function that failed to produce closed-form solutions was solved through the use of a numerical method to approximate the optimal portfolio weight. Furthermore, numerical simulations were performed using data from two stocks in the e-commerce sector to prove the impact of parameter changes on investment decisions. The result showed explicit analytical values for each utility function, providing investors with a structured framework for determining optimal portfolio weights consistent with their risk profile. Full article

This academic article seeks to clarify the state of the literature on a very pertinent topic that is based on how digital innovation, considering emerging technologies and how they could be used in business management and marketing, could increase sustainable development. The sustainable economy, which should maintain long-term development through efficient resource management, has as allies emerging technologies such as artificial intelligence, blockchain, and the Internet of Things that can help reduce waste, reduce the carbon footprint, and automate tasks. Additionally, they could present themselves as a solution to improve aspects of digital communication between companies and their consumers in remote training, distribution chain, e-commerce, and process optimization in different sectors of activity. These advances will, on the one hand, allow the possibility of conducting a greater amount of professional training, increasing the number of qualified professionals and, on the other hand, facilitate trade exchanges, promoting the economy. Based on a systematic bibliometric review of the literature using the PRISMA framework, this study investigates how digital tools catalyze transformative changes in different sectors of activity. The results indicate that, overall, the academic articles analyzed in this literature review present studies focused on digitalization and sustainability (approximately 50%). In second place are topics related to digitalization and other topics such as: smart cities; Sustainable Development Goals; academia; the digital economy; government policies; academic education; and sustainable communication (29%). Finally, in third place, there are academic articles closely linked to digitalization and the environment, more specifically to sustainable practices and the management of natural resources (21%). The article concludes that digital development, when used wisely, serves as a crucial lever to address the world’s most pressing sustainability imperatives. Future research should emphasize interdisciplinary collaboration and adaptive governance to ensure that these digital changes produce lasting impacts for people and the planet. Full article

The rapid development of e-commerce has brought unprecedented opportunities for small and medium-sized enterprises (SMEs), particularly those in the retail sector. WeChat e-commerce, which combines the advantages of social media and e-commerce, offers SME retailers an accessible and efficient e-commerce solution. As market competition intensifies, user retention has become crucial for their success in WeChat e-commerce. This study extended the Expectation Confirmation Model (ECM) by incorporating perceived quality and privacy calculus to examine users’ continuance intention towards WeChat e-commerce for SME retailers. A total of 694 valid responses were collected from users with prior experience using WeChat e-commerce offered by SME retailers, and the proposed model was validated using structural equation modeling. The results indicated that the trade-off between privacy concerns and perceived benefits significantly affected users’ intention to continue using WeChat e-commerce. Moreover, the information quality and service quality dimensions were found to directly or indirectly influence this continuance intention towards WeChat e-commerce via privacy concerns, perceived benefits, and confirmation. In conclusion, this study provides insights for further research on continuance intention in WeChat e-commerce and suggestions for SMEs to formulate e-commerce strategies. Full article

Achieving organizational efficiency requires the selection of an appropriate operating model. To date, no objective indicators, methods of measuring, or criteria for evaluating the effectiveness and efficiency of forest management organizations have been developed. In the heterogeneous forest management of the European Union (EU), multiple objectives and functions—from production to social and ecological services—coexist at regional and national levels. This study provides an overview of the organizational and legal forms of EU forestry, taking into account environmental conditions, ownership structures, and the role of the forestry sector in national economies. The legal information of EU countries on forest management was verified. We examine the impact of the entity’s organizational and legal form on the implementation of sustainable forest management and the objectives of the New EU Forest Strategy 2030, particularly in terms of absorbing external capital for forest protection and climate-related activities. Joint stock companies, public institutions, and enterprises are the most relevant. The private sector is dominated by individual farms, associations, chambers of commerce, and federations. A clear trend toward transforming state-owned enterprises into joint-stock companies and expanding their operational scope has been confirmed. Multifunctional forest management is practiced in both state and private forests. Economic efficiency, legal and property liability, and organizational goals depend on the chosen organizational and legal form. Full article

Automation technologies play a pivotal role in optimizing logistics operations within warehouse facilities. Retail companies invest in these technologies to maintain pace with the customers demands by increasing their production capacity while reducing their financial expenses. In this paper, we conduct a study on warehouse automation in the European e-commerce sector by analyzing historical data from three fulfillment centers. Accordingly, we explore diverse data science approaches applied to trained machine learning models to determine the automatons that have the greatest impact on financial costs. The purpose is to support supply chain managers in identifying the most profitable logistics automatons that merit consideration in future automation projects. The study offers a comprehensive analysis that encourages e-commerce companies to invest in tailored automation for future warehouse installations. Full article

Against the backdrop of rapid rail transit development, this study takes Urumqi Metro Line 1 as a case, using geographic information system (GIS) spatial analysis and space syntax Pearson correlation coefficient methods. Focusing on an 800 m radius around station areas, the research investigates the distribution characteristics of commercial facilities and the impact of metro development on commercial patterns through the quantitative analysis and distribution trends of points of interest (POI) data across different historical periods. The study reveals that following the opening of Urumqi Metro Line 1, commercial facilities have predominantly clustered around stations including Erdaoqiao, Nanmen, Beimen, Nanhu Square, Nanhu Beilu, Daxigou, and Sports Center, with kernel density values surging by 28–39%, indicating significantly enhanced commercial agglomeration. Metro construction has promoted commercial POI quantity growth and commercial sector enrichment. Surrounding commercial areas have developed rapidly after metro construction, with the most significant impacts observed in the catering, shopping, and residential-oriented living commercial sectors. After the construction of the subway, the distribution pattern of commercial facilities presents two kinds of aggregation patterns: one is the original centripetal aggregation layout before construction and further strengthened after construction; the other is the centripetal aggregation layout before construction and further weakened after construction, tending to the site level of face-like aggregation. The clustering characteristics of different business types vary. Factors such as subway accessibility, population density, and living infrastructure all impact the distribution of businesses around the subway. The impact of subway accessibility on commercial facilities varies by station infrastructure and urban area. The findings demonstrate how transit infrastructure development can catalyze sustainable urban form evolution by optimizing spatial resource allocation and fostering transportation–commerce synergy. It provides empirical support for applying the theory of transit-oriented development (TOD) in the urban planning of western developing regions. The research not only fills a research gap concerning the commercial space differentiation law of metro systems in megacities in arid areas but also provides a scientific decision-making basis for optimizing the spatial resource allocation of stations and realizing the synergistic development of transportation and commerce in the node cities along the “Belt and Road”. Full article

The emergence of Unmanned Aerial Vehicles (UAVs), commonly known as drones, has presented numerous transformative opportunities across sectors such as agriculture, commerce, and security surveillance systems. However, the proliferation of these technologies raises significant concerns regarding security and privacy, as they could potentially be exploited for unauthorized surveillance or even targeted attacks. Various research endeavors have proposed drone detection models for security purposes. Yet, deploying these models on edge devices proves challenging due to resource constraints, which limit the feasibility of complex deep learning models. The need for lightweight models capable of efficient deployment on edge devices becomes evident, particularly for the anonymous detection of drones in various disguises to prevent potential intrusions. This study introduces a lightweight deep learning-based drone detection model (LDDm-CNN) by fusing knowledge distillation with Bayesian optimization. Knowledge distillation (KD) is utilized to transfer knowledge from a complex model (teacher) to a simpler one (student), preserving performance while reducing computational complexity, thereby achieving a lightweight model. However, selecting optimal hyper-parameters for knowledge distillation is challenging due to a large number of search space and complexity requirements. Therefore, through the integration of Bayesian optimization with knowledge distillation, we present an enhanced CNN-KD model. This novel approach employs an optimization algorithm to determine the most suitable hyper-parameters, enhancing the efficiency and effectiveness of the drone detection model. Validation on a dedicated drone detection dataset illustrates the model’s efficacy, achieving a remarkable accuracy of 96% while significantly reducing computational and memory requirements. With just 102,000 parameters, the proposed model is five times smaller than the teacher model, underscoring its potential for practical deployment in real-world scenarios. Full article

In today’s evolving society, meaningful development cannot be fully realized without acknowledging the vital role of the electronics sector, especially as it functions within informal markets. These markets have become more than just centers of commerce; they serve as informal learning grounds where many young people acquire entrepreneurial skills, develop resilience, and find alternatives to social vices. For many, informal entrepreneurship is not just an option but a means of survival and self-empowerment. Despite their growing relevance, the link between the entrepreneurial abilities nurtured in these informal markets and actual business performance has not been adequately examined. This study, therefore, aimed to explore how informal electronics entrepreneurs in a developing economy navigate their environment, overcome challenges, and create wealth through vision, innovation, and calculated risk-taking. Anchored in institutional theory, the research employed a qualitative approach, using cluster, purposive, and simple random sampling to select participants from key informal business units. Interviews were conducted, transcribed, and analyzed using QSR NVivo 12, allowing for deep insight into the lived experiences of the entrepreneurs. Findings revealed that 78% of participants emphasized practical suggestions that aid informal business survival, such as customer-driven innovations, adaptive strategies, and avoiding confrontations with regulatory agencies. Key attributes such as foresight, adaptability, and risk management accounted for 66% of the variance in corporate success. Strategic and innovative approaches are enabling informal firms to endure and prosper, since 61% of respondents associated these competencies with organizational success. The new BSP framework, which integrates institutional and contingency theories, illustrates how informal enterprises endure by conforming to or opposing institutional pressures and adjusting to environmental changes. The results indicate that, when properly understood and supported, the informal electronics sector may develop sustainably. This study demonstrates that informal entrepreneurship is influenced by formal regulations, informal norms, and local enforcement mechanisms, therefore enhancing institutional theory and elucidating business behavior in developing nations. The Business Survival Paradigm [BSP] illustrates how informal enterprises navigate institutional obstacles to endure. It advocates for policies that integrate the official and informal sectors while fostering sustainable development. The paper advocates for ongoing market research to assist informal firms in remaining up-to-date. It implores authorities to acknowledge the innovative potential of the informal sector and to provide supportive frameworks for sustainable growth and formal transition where feasible. Full article

Consumer satisfaction critically determines the operational sustainability of fresh food e-commerce platforms, yet integrated investigations combining multi-source data remain scarce. This study develops a theory–data fusion framework to identify key satisfaction drivers in China’s fresh e-commerce sector. Utilizing Python-based crawlers, we extracted 1252 online reviews of Aksu apples from a certain fresh produce e-commerce platform alongside 509 validated questionnaires. Through systematic literature synthesis, three core dimensions—perceived value (price–performance balance), platform experience (interface usability), and perceived quality (freshness assurance)—were operationalized into measurable indicators. The final structural equation model reveals that perceived value, platform experience, and perceived quality all have significant positive impacts on consumer satisfaction. This study pioneers a methodological paradigm integrating computational text mining (Octopus Collector + SPSS Pro) with traditional psychometric scales, achieving superior model fit (RMSEA = 0.023, CFI = 0.981). These findings empower platforms to implement a precision strategy. The validated framework provides a theoretical basis for omnichannel consumer research while addressing the data-source bias prevalent in prior studies. Full article

Influencer marketing has emerged as a crucial element in digital marketing, significantly shaping consumer behavior and online shopping preferences. This study examined the multidimensional impact of influencer marketing by analyzing engagement metrics, marketing effectiveness, and consumer decision-making processes, based on consumers in the Greek sector. Through a structured methodological framework, the research employed a questionnaire-based survey, statistical modeling, and Fuzzy Cognitive Mapping (FCM) scenarios to assess consumer interactions with influencer-driven content. Findings highlight that while influencer marketing enhances brand engagement and sales, its effectiveness varies based on content authenticity, transparency, and user trust. Additionally, consumer purchasing decisions are influenced by social media visibility, personalized marketing strategies, and digital platform usability. This study underscores the need for strategic influencer selection and information-driven marketing optimization to sustain long-term consumer engagement. These insights provide practical implications for businesses aiming to enhance digital marketing strategies and contribute to the ongoing discourse on social commerce and consumer-centric marketing models. Full article

Common prosperity is the fundamental driving force of rural revitalization, as well as the foundation for achieving sustainable economic development. The e-commerce to the countryside policy has energized the rural economy, helping to improve household economic resilience and reduce income stratification, thereby promoting the inclusive and sustainable development of the digital economy. Drawing on panel data collected from rural fixed observation points in Henan Province during 2009–2022, this study employs a staggered difference-in-differences (DID) approach to evaluate the impact of China’s e-commerce to the countryside policy on farmers’ income and income inequality. The empirical results reveal that the rural e-commerce policy significantly increases farmers’ income while mitigating income inequality. The underlying mechanisms function through three synergistic pathways: industrial structural upgrading, manifested through tri-sector integration driven by rural enterprise development; factor allocation restructuring, evidenced by productivity gains from optimized labor–capital reallocation; and enhanced market inclusion through digital technology empowerment that lowers participation barriers. Heterogeneity analysis indicates that the e-commerce to the countryside policy exhibits pro-poor characteristics, with its income-enhancing and equalizing effects being particularly pronounced in agricultural areas, traditional villages, county-level civilized villages, underdeveloped regions, registered poverty-stricken villages, and households with low human, physical, and financial capital endowments. These findings confirm the inclusive development efficacy of rural e-commerce among vulnerable populations. Consequently, the study provides a replicable policy implementation framework for achieving common prosperity objectives. Full article