Search Results (4,106)

The fusion of livestreaming e-commerce and AI technology is booming, and many firms have started to replace human streamers with AI streamers. Despite their popularity, the acceptance of AI streamers by consumers varies widely and the signaling effects of AI streamers still remain unclear. We build an analytical model and compare scenarios where the acceptance level is either exogenously given or endogenously determined, highlighting the implications for firms’ optimal separation strategy. Our findings suggest that in markets with moderate information asymmetry, using both price and acceptance level as joint signals can be more profitable for high-quality firms. Conversely, in highly asymmetric markets, firms must incur additional costs to distinguish their high-quality products, regardless of the signaling strategy employed. Our paper provides strategic insights for firms aiming to leverage AI streamers in diverse market conditions. Full article

In the context of global carbon emission reduction (CER) targets and slowing economic growth, it is imperative for suppliers to make informed choices regarding CER and financing strategies. However, limited research has explored the impact of financing strategies on CER. This paper develops a supply chain model that includes a supplier, a manufacturer, an E-commerce platform (E-C platform), and consumers with a preference for low-carbon products. The supplier sets the wholesale price, while the manufacturer controls both the production quantity and the unit amount of CER. We examine whether the manufacturer will invest in CER with sufficient capital or under various financing scenarios, namely (1) traditional production with sufficient capital (Scenario ST); (2) CER implementation with sufficient capital (Scenario SG); (3) CER implementation with E-C platform financing (Scenario EG); (4) CER implementation with bank financing (Scenario BG). Through comparative analysis, the analysis reveals that, regardless of the financing method chosen, the supplier’s profit and the manufacturer’s production quantity increase when the manufacturer invests in CER technology innovation compared to the traditional scenario. Furthermore, in terms of the manufacturer’s profit, if the service cost of bank financing exceeds a certain threshold, the manufacturer should either seek financing from the E-C platform or abandon the CER investment. Additionally, with respect to CER outcomes, Scenario BG outperforms Scenario EG. These findings provide a theoretical foundation and decision-making support for supply chain participants when addressing carbon reduction and financing strategy decisions. Full article

As creative tourism consolidates both as an autonomous research area and a valid sustainable form of tourism based on learning, active participation, co-creation, creative self-expression, and local community engagement, its economic models have evolved from simple creative activities to complex territorial and technological-based networks, boosting the entrepreneurship of creative communities in tourism. Seen as a means of territorial development strategy through tourism, creative networks can stem from private partnerships, the public sector, the local community, or third sector organisations. Focusing on a case study approach, this research adopts a multidimensional framework with the goal of analysing the benefits of creative tourism towards a creative tourism network. Using a quantitative approach through Likert scale statements of five items, this research aimed to study the creative supply and demand linked to a creative tourism network. Based on the principles and benefits of creative tourism and the goals of the creative network, the following dimensions were analysed: job creation; production and commerce of creative products; safeguarding of tangible and intangible heritage; development of social capital between network agents; creative and innovative images of the region; the active role of the local community in the network; and the consumption profile of the creative tourist through the network. The main conclusions of the study point to the network boosting local job creation and preserving local traditions but struggling to expand sales and increase tourist stays. While it enhances the region’s image, there is a need for stronger collaboration and community engagement. Full article

The widespread popularization of Internet technology has facilitated the emergence of e-commerce as a novel avenue for agricultural product sales, driven by its convenience and broad reach. Nevertheless, in Wuchang City, a well-developed agricultural region in northeastern China, some farmers still exhibit low enthusiasm for participating in agricultural product e-commerce, with limited levels of engagement. To investigate the underlying causes, this study analyzes survey data from 301 farmers in Wuchang City and uses mean difference significance tests and Logistic and Tobit regression models to explore the factors influencing farmers’ adoption of e-commerce for agricultural products. The results demonstrate that gender and the number of household members involved in agricultural labor significantly influence the adoption decision and the extent of adoption. There is a significant difference in the adoption of decisions among ages. Subjective willingness and policy perception positively and significantly influence the adoption decision. Risk perception significantly and negatively impacts the extent of adoption. Infrastructure exerts a significant and negative influence on the adoption decision but a significant and positive influence on the extent of adoption. Based on these findings, this study suggests localized reforms, enhanced e-commerce promotion, and differentiated training to boost farmers’ adoption, promoting sustainable development of the agricultural e-commerce economy. Full article

The increasing demand for consumers to engage in e-commerce “anytime, anywhere” necessitates more advanced and integrated solutions. This paper presents a novel approach for integrating e-commerce platforms with the Internet of Things (IoT) through the use of agent-based models. The key objective is to create a multi-agent system that optimizes interactions between IoT devices and e-commerce systems, thereby improving operational efficiency, adaptability, and user experience in online transactions. In this system, independent agents act as intermediaries, facilitating communication and enabling decentralized decision making. This architecture allows the system to adjust dynamically to environmental changes while managing complex tasks, such as real-time inventory monitoring and personalized product recommendations. The paper provides a comprehensive overview of the system’s framework, design principles, and algorithms, highlighting the robustness and flexibility of the proposed structure. The effectiveness of this model is validated through simulations and case studies, demonstrating its capacity to handle large data volumes, ensure security and privacy, and maintain seamless interoperability among a variety of IoT devices and e-commerce platforms. The findings suggest that this system offers a viable solution to the challenges of integrating IoT into e-commerce, contributing to both academic research and practical applications in the field. Full article

This study examines the impact of remote audit quality (RAQ) on the quality of audit work (QAW). Further, it explores the moderating effect of both client technological readiness (CLTR) and auditor technology readiness (ADTR) on the link between RAQ and QAW. Data were collected through a questionnaire survey distributed to all external auditors working in Egypt. The final sample consists of 280 auditors. The data were analyzed with smart partial least squares (Smart-PLS) software. The results showed that RAQ has a positive and significant impact on QAW. Moreover, the results revealed that CLTR and ADTR moderate the relationship between RAQ and QAW. CLTR was found to have a positive moderating role, as CLTR was found to strengthen the relationship between RAQ and QAW, while ADTR was found to have a negative moderating role, as ADTR was found to weaken the relationship between RAQ and QAW. The findings can provide a pivotal yardstick for guiding companies, auditing firms, auditing professional bodies, and regulators in the Egyptian context. Positioned as one of the early studies to concentrate on the moderating role of CLTR and ADTR in the relationship between RAQ and QAW, this research suggests insights within an emerging market context. Full article

The rapid growth of e-commerce has significantly increased demands on logistics providers, particularly in the areas of product handling and shipment. One of the most challenging and critical aspects of this process is the handling of dangerous goods (DG). This is a complex and time-intensive operation that requires safety measures and proper packaging and labelling, because mishandling DG can lead to severe injuries, property loss, and legal liability. This puts additional pressure on logistics providers to efficiently manage DG while maintaining speed and safety in the context of e-commerce. To meet this challenge, there is an urgent need to advance training programs and enhance the operational efficiency for DG handling. The use of mixed reality (MR) technology offers a promising solution. By seamlessly integrating virtual elements with real-world environments, MR has the potential to greatly improve the effectiveness and efficiency of the training of DG handling. Earlier research has examined MR in various fields, while there is still a research gap in applying MR specifically to the training of DG handling. This paper seeks to address the current research gap by presenting a novel MR model, named “MRSafe,” for a training program on the safe handling of DG. The model offers users virtual experiences and comprehensive guidance to provide operational decision support. Full article

This paper uses data from the 24 governorates in Tunisia over the period 2012–2020 to study the relationship between job insertion of higher education graduates into the formal labor market and a number of independent variables, namely active labor supply, labor demand, an active labor market policy program (named the CIVP program), and the waiting time for job insertion. The balanced panel, which includes 216 observations for each variable, was the basis of different tests and estimations. The results of the tests allowed the assessment of a fixed effects model and a long-term relationship using FMOLS and VECM models. Results show that, in the long term, active labor supply and the CIVP program have positive effects on the job insertion of higher education graduates. In contrast, the results in the short term do not appear significant, with a negative effect of the CIVP program that reflects the fact that companies exploit most of the benefits of this wage subsidy program on job insertion before final recruitment. Using the ARDL model, the individual results by governate show specific differences across areas. Full article

Artificial intelligence serves as the fundamental catalyst for a new wave of technological innovation and industrial transformation. It holds vital importance in reaching carbon reduction targets and the objectives of “carbon peak and neutrality”. This factor contributes significantly to the reduction in carbon emissions in the industrial domain. This article utilizes panel data from 30 provinces in China, covering the years 2013 to 2021, to develop an evaluation framework for assessing the progress of artificial intelligence development. Through the use of double fixed-effect models, mediation effect models, and threshold effect models, the empirical analysis examines the industrial carbon reduction effects of artificial intelligence and its operating mechanisms. Research indicates that the advancement of AI can significantly reduce carbon emission intensity within the industrial sector. This conclusion remains valid following comprehensive robustness tests. Furthermore, there exists temporal and regional variability in AI’s impact on industrial carbon reduction, particularly more pronounced after 2016 and in central and western regions. AI influences carbon emission reduction in China’s industrial sector through the advancement and optimization of industrial structures. Here, the increase in senior-level operations acts as a partial masking effect, while optimization serves as a partial mediator. The relationship between AI and industrial carbon emission intensity is non-linear, being influenced by the threshold of government intervention; minimal intervention weakens AI’s effect on carbon intensity reduction. These findings enhance our understanding of the factors influencing industrial carbon emissions and contribute to AI-related research. They also lay a solid empirical groundwork for promoting carbon emission reduction in the industrial domain via AI. Additionally, the results offer valuable insights for formulating policies aimed at the green transformation of industry. Full article

Most existing two-lane traffic flow lattice models fail to fully consider the interactions between drivers’ aggressive lane-changing behaviors and their desire for smooth driving, as well as their combined effects on traffic dynamics. To fill this research gap, under symmetric lane-changing rules, this paper proposes a novel two-lane lattice model that incorporates these two factors as co-influencers. Based on linear and nonlinear stability analyses, we derive the linear stability conditions of the new model, along with the density wave equation and its solutions describing traffic congestion propagation near critical points. Numerical simulations validate the theoretical findings. The results indicate that in the two-lane framework, enhancing either drivers’ lane-changing aggressiveness or introducing the desire for smooth driving alone can somewhat improve traffic flow stability. However, when considering their synergistic effects, traffic flow stability is enhanced more significantly, and the traffic congestion is suppressed more effectively. Full article

Background/Objectives: Breast cancer, a leading health threat affecting millions worldwide, requires effective therapeutic interventions. Celastrol (CEL), despite its antitumor potential, is limited by poor solubility and stability. This study aimed to enhance CEL’s efficacy by encapsulating it within glycyrrhizic acid (GA)-modified lipid calcium carbonate (LCC) nanoparticles for targeted breast cancer therapy. Methods: The 4T1 mouse breast cancer cells were used for the study. GA-LCC-CEL nanoparticles were prepared using a gas diffusion method and a thin-film dispersion method. GA-LCC-CEL were characterized using the zeta-potential, dynamic light scattering and transmission electron microscope (TEM). The in vitro release behavior of nanoparticles was assessed using the in vitro dialysis diffusion method. Cellular uptake was examined using flow cytometry and confocal microscopy. Intracellular ROS and Rhodamine 123 levels were observed under fluorescence microscopy. MTT and colony formation assays assessed cytotoxicity and proliferation, and apoptosis was analyzed by Annexin V-FITC/PI staining. Wound healing and transwell assays evaluated migration, and Western blotting confirmed protein expression changes related to apoptosis and migration. Results: GA-LCC-CEL nanoparticles displayed a well-defined core-shell structure with a uniform size distribution. They showed enhanced anti-proliferative and pro-apoptotic effects against 4T1 cells and significantly reduced breast cancer cell invasion and migration. Additionally, GA-LCC-CEL modulated epithelial-mesenchymal transition (EMT) protein expression, downregulating Snail and ZEB1, and upregulating E-cadherin. Conclusions: GA-LCC-CEL nanoparticles represent a promising targeted drug delivery approach for breast cancer, enhancing CEL’s antitumor efficacy and potentially inhibiting cancer progression by modulating EMT-related proteins. Full article

High-dimensional sparse matrix data frequently arise in various applications. A notable example is the weighted word–word co-occurrence count data, which summarizes the weighted frequency of word pairs appearing within the same context window. This type of data typically contains highly skewed non-negative values with an abundance of zeros. Another example is the co-occurrence of item–item or user–item pairs in e-commerce, which also generates high-dimensional data. The objective is to utilize these data to predict the relevance between items or users. In this paper, we assume that items or users can be represented by unknown dense vectors. The model treats the co-occurrence counts as arising from zero-inflated Gamma random variables and employs cosine similarity between the unknown vectors to summarize item–item relevance. The unknown values are estimated using the shared parameter alternating zero-inflated Gamma regression models (SA-ZIG). Both canonical link and log link models are considered. Two parameter updating schemes are proposed, along with an algorithm to estimate the unknown parameters. Convergence analysis is presented analytically. Numerical studies demonstrate that the SA-ZIG using Fisher scoring without learning rate adjustment may fail to find the maximum likelihood estimate. However, the SA-ZIG with learning rate adjustment performs satisfactorily in our simulation studies. Full article

This study aims to detect starch adulteration in dairy products utilizing an artificial neural network (ANN) model. Globally, milk fraud represents a significant challenge to food safety, posing substantial health risks to consumers. In this context, spectral data derived from milk samples with varying starch concentrations were processed using feature scaling and normalization techniques. The ANN model was rigorously trained and validated employing the stratified k-fold cross-validation method, demonstrating exceptional proficiency in detecting starch-adulterated milk samples and effectively differentiating among various starch concentrations. The principal findings indicate that the model achieved 100% accuracy, coupled with high levels of precision, sensitivity, and F1-scores. Future research should explore the application of this model to different types of adulteration and extend its validation on larger datasets. Furthermore, the potential adaptability of this method for other food products and field applications warrants investigation. This study offers valuable insights for milk producers, food safety professionals, and consumers, particularly highlighting the implications for small-scale rural farms, thereby enriching the discourse on food safety within short food supply chains. Full article

With the rapid development of mobile applications (apps), various types of mobile apps have become the main channels for smartphone interaction. The user’s app switching behavior in mobile search tasks has also received attention from academia. This article uses the push–pull–mooring (PPM) theoretical model to determine the three influencing factors of push, pull, and mooring that affect user’s app switching behavior in mobile search. Data were collected from 374 respondents using a structural equation model. This study can deepen the understanding of app switching in user mobile search from the perspectives of information source selection, user information search path, etc. This study found that in terms of pushing factors, the complexity of search tasks positively affects users’ willingness to switch apps. In terms of pulling factors, the attractiveness of alternative products and users’ follow-up activities will positively affect their switching willingness. Meanwhile, inertia serves as a mooring variable to regulate the relationship between push-pull factors and user switching intentions. This research highlights key insights on user behavior, follow-up activities, and the role of switching costs and inertia, contributing to the broader literature on information-seeking behavior. It also provides actionable recommendations for app developers to enhance search experiences and retain users by integrating personalized, multi-modal features. Full article

In the age where environmental sustainability issues are progressively prioritized, green demarketing has risen as a strategic choice for organizations aiming to decrease customer demand for unsustainable services/products and stimulate more eco-friendly substitutes. This paper investigates the impact of green demarketing on brand credibility, green authenticity, and perceptions of greenwashing. This paper examines how restaurants that are engaged in green demarketing practices are perceived with regard to their commitment toward the environment and whether such practices improve or reduce a brand’s credibility. Moreover, this study explores green authenticity and explores how an organization’s brand looks in its sustainability practices when employing demarketing activities. The proper consequences of greenwashing, where customers might perceive these tactics as insincere or misleading, are also significantly explored. By employing a survey research method, 414 restaurant customers were targeted, and the gathered data were analyzed employing partial least square structural equation modeling (PLS-SEM). This study’s results might contribute to the increasing interest in sustainable marketing activities and deliver practical implications for restaurants aiming to navigate the complex multi-dynamics of ecofriendly responsibility and consumer credibility. Full article