Search Results (13,649)

In the era of digital marketing, where consumers and enterprises frequently interact with each other, consumers hold different attitudes toward the different sources of information, including corporate social responsibility information. Negative corporate social responsibility can have direct impacts on corporate reputation. Choosing appropriate channels to publish the negative social responsibility information of enterprises in order to reduce the impact of these negative social events on corporate reputation is imperative for corporate image management. This research examines the differences in the impact of enterprise-generated content and -co-generated content on consumer attitudes using second-hand data analysis and then investigates how different information sources influence corporate reputation through empirical experiments. The results indicate that co-generated content performs better than other sources of information on corporate reputation, while professional user-generated content has the most negative impact. We further identify the external attribution as a mediation mechanism in the relationship between information sources and corporate reputation. The theoretical contributions and managerial implications of the research findings are discussed. Full article

Rising costs, complex supply chain management, and stringent regulations have created significant financial burdens on business sustainability, calling for new and rapid strategies to help enterprises transform. Supply chain digitalization (SCD) has emerged as a promising approach in the context of digitalization and globalization, with the potential to reduce an enterprise’s debt costs. Developing a strategic framework for SCD that effectively reduces the cost of debt (CoD) has become a key academic challenge, critical for ensuring business sustainability. To this end, under the perspective of four flows, SCD is deconstructed into four distinct features: logistics flow digitalization (LFD), product flow digitalization (PFD), information flow digitalization (IFD), and capital flow digitalization (CFD). To precisely measure the four SCD features and the dependent variable, COD, publicly available data from Chinese listed manufacturing enterprises such as annual report texts and financial statement data are collected, and various data mining technologies are also used to conduct data measurement and data processing. To comprehensively investigate the impact pattern of SCD on CoD, we employed the explainable machine learning methodology for data analysis. This methodology involved in-depth data discussions, cross-validation utilizing a series of machine learning models, and the utilization of Shapley additive explanations (SHAP) to explain the results generated by the models. To conduct sensitivity analysis, permutation feature importance (PFI) and partial dependence plots (PDPs) were also incorporated as supplementary explanatory methods, providing additional insights into the model’s explainability. Through the aforementioned research processes, the following findings are obtained: SCD can play a role in reducing CoD, but the effects of different SCD features are not exactly the same. Among the four SCD features, LFD, PFD, and IFD have the potential to significantly reduce CoD, with PFD having the most substantial impact, followed by LFD and IFD. In contrast, CFD has a relatively weak impact, and its role is challenging to discern. These findings provide significant guidance for enterprises in furthering their digitalization and supply chain development, helping them optimize SCD strategies more accurately to reduce CoD. Full article

Technological progress and digitalization have ushered in significant transformations in business strategies. At present, research is scarcely focused on the influence of the adoption of digital technologies (DTs) on establishing comprehensive relationships within the context of a circular economy (CE), and the supply chain (SC) framework to contribute to the Resource-Based View (RBV) theory. This study utilizes survey data collected from 235 manufacturing practitioners employed by Turkish manufacturing enterprises to explore a model elucidating the relationship between DTs adoption and sustainability performance (SP) through supply chain traceability (SCT), supply chain resilience (SCR), and circular economy practices (CEPs), based on 10R strategies. Through this linkage, this research accentuates that the exclusive integration of CEPs with digital technology solutions is insufficient for industrial enterprises to attain their long-term sustainability goals. It underscores the necessity of ensuring SCT and/or SCR in this context. Full article

Background/objectives: Iron deficiency anemia is common in patients with colorectal cancer and affects postoperative outcomes. Preoperative intravenous iron supplementation corrects anemia effectively; however, the effect on the postoperative clinical course is unclear. The aim of this study was to investigate the effects of implementing a screening program for iron deficiency anemia and correction in patients with colorectal cancer. Methods: This was a retrospective single-institutional quality-assurance study that included patients undergoing elective surgery for colorectal cancer between January 2019 and December 2021. On 1 March 2020, screening for iron deficiency was implemented. Anemia was corrected by intravenous ferricarboyxmaltose. Patients with hemoglobin <7mmol/L and ferritin <30 µg/L or ferritin 30–100 µg/L and TSAT < 0.2 were divided into iron- and non-iron groups. The primary outcome was a change in hemoglobin concentration, and secondary outcomes included blood transfusion, complications, length of stay, mortality, and implementation efficacy. Data from the patients were extracted from digital patient charts and entered into a database. Results: A total of 532 patients were included, of which 177 patients (33.3%) were anemic, 63 were in the iron group, and 114 were in the non-iron group. Adherence to the screening program was 86.3%. Hemoglobin (iron group) increased from a mean of 5.7 mmol/L (SD 0.8) to 6.9 (0.8) mmol/L, p < 0.001. Iron treatment decreased transfusion rates from 27.7% to 9.1%, p = 0.007, and increased the rate of patients without complications from 53.2% to 79.6%, p < 0.001, which held true after controlling for confounding. In addition, 90-day mortality was lower in the iron group; however, this was not significant. Conclusions: Administration of intravenous iron increased hemoglobin, leading to reduced use of blood transfusion and fewer complications. Full article

Socialized services are crucial for addressing the issue of “who will farm the land” and subsequently enhancing agricultural economic resilience (AER). However, few studies have examined the mechanisms and effects of socialized services on AER. Consequently, this study aims to elucidate the impact and mechanisms of socialized services on AER, with the objective of providing new policy recommendations for enhancing AER and ensuring food security. Based on provincial panel data from China spanning 2009 to 2021, this paper examines the impact and mechanisms of socialized services on AER using a two-way fixed effects model, a mediated effects model, and a panel threshold model. The findings reveal that socialized services significantly enhance AER. Mechanism analysis indicates that socialized services enhance AER by accelerating the substitution of machinery for manpower and promoting the efficiency of labor division. Heterogeneity analysis indicates that in regions with high grain cropping ratios and high internet penetration rates, the enhancement effect of socialized services on AER is stronger. Further analysis uncovers a significant nonlinear threshold effect of socialized services on AER. The impact becomes more pronounced when AER surpasses 0.4689. Consequently, this study argues that in the process of constructing a modern agricultural business system, it is essential to focus on improving the differentiated socialized service system and accelerating the development of rural digital infrastructure. Full article

In the context of digitization and automation, extracting relevant information from business documents remains a significant challenge. It is typical to rely on machine-learning techniques to automate the process, reduce manual labor, and minimize errors. This work introduces a new model for extracting key values from electricity invoices, including customer data, bill breakdown, electricity consumption, or marketer data. We evaluate several machine learning techniques, such as Naive Bayes, Logistic Regression, Random Forests, or Support Vector Machines. Our approach relies on a bag-of-words strategy and custom-designed features tailored for electricity data. We validate our method on the IDSEM dataset, which includes 75,000 electricity invoices with eighty-six fields. The model converts PDF invoices into text and processes each word separately using a context of eleven words. The results of our experiments indicate that Support Vector Machines and Random Forests perform exceptionally well in capturing numerous values with high precision. The study also explores the advantages of our custom features and evaluates the performance of unseen documents. The precision obtained with Support Vector Machines is 91.86% on average, peaking at 98.47% for one document template. These results demonstrate the effectiveness of our method in accurately extracting key values from invoices. Full article

Information dissemination and preservation are crucial for societal progress, especially in the technological age. While technology fosters knowledge sharing, it also risks spreading misinformation. Audio deepfakes—convincingly fabricated audio created using artificial intelligence (AI)—exacerbate this issue. We present Sonic Sleuth, a novel AI model designed specifically for detecting audio deepfakes. Our approach utilizes advanced deep learning (DL) techniques, including a custom CNN model, to enhance detection accuracy in audio misinformation, with practical applications in journalism and social media. Through meticulous data preprocessing and rigorous experimentation, we achieved a remarkable 98.27% accuracy and a 0.016 equal error rate (EER) on a substantial dataset of real and synthetic audio. Additionally, Sonic Sleuth demonstrated 84.92% accuracy and a 0.085 EER on an external dataset. The novelty of this research lies in its integration of datasets that closely simulate real-world conditions, including noise and linguistic diversity, enabling the model to generalize across a wide array of audio inputs. These results underscore Sonic Sleuth’s potential as a powerful tool for combating misinformation and enhancing integrity in digital communications. Full article

Many real-world networks for the Industrial Internet of Things (IIoT) have diverse connectivity characteristics and real-time constraints imposed by industrial processing. In the context of digital twin networks (DTNs), a large number of IIoT devices may access the network and have a tremendous volume of data. A crucial element of these IIoT devices is mobility, which cannot be effectively solved because the number of IIoT devices connected is extremely large. IIoT devices in DTNs suffer from poor data transmission and link quality because of their mobility. In this paper, device-to-device (D2D) communication-based mobility-assisted digital twin networks are proposed, where edge computing is introduced to design an efficient mapping between the physical and virtual space. Then, we propose the architecture of data transmission for the D2D network to maximize the data rate for reliable connectivity among multiple mobile nodes based on IIoT. A Markov decision process (MDP) is formulated to maximize the data rate for multiple mobile nodes while maintaining the D2D communication link quality. The simulation results demonstrate the superiority of the proposed scheme over other comparable models. Full article

To monitor temperature as a function of varying inductance and resistance, we propose a data-driven digital twin approach for the rapid and efficient real-time estimation of the rotor temperature in an induction motor. By integrating differential equations with online signal processing, the proposed data-driven digital twin approach is structured into three key stages: (1) transforming the nonlinear differential equations into discrete algebraic equations by substituting the differential operator with the difference quotient based on the sampled voltage and current; (2) deriving approximate analytical solutions for rotor resistance and stator inductance, which can be utilized to estimate the rotor temperature; and (3) developing a general procedure for obtaining approximate analytical solutions to nonlinear differential equations. The feasibility and validity of the proposed method were demonstrated by comparing the test results with a 1.5 kW AC motor. The experimental results indicate that our method achieves a minimum estimation error that falls within the standards set by IEC 60034-2-1. This work provides a valuable reference for the overheating protection of induction motors where direct temperature measurement is challenging. Full article

This research intends to evaluate the effect on the performance of small and medium enterprises (SMEs) of technological developments, including digital marketing, and to determine the variables that affect the relationship. Because these businesses are essential to driving local economies, boosting local economic growth, and creating job opportunities, this is a very important question, and figuring out how to best help them succeed through digital marketing is critical. The data were generated from a sample of 190 marketing companies, and each of the managers of these companies was surveyed for the information needed. This research focuses on the impact of SMEs’ performance through digital marketing strategies, including online advertising, social media marketing, search engine optimization (SEO), and customer engagement through digital channels. A further analysis was performed looking at the mediating effect of digital transformation on the relationship between digital marketing and firm performance. The theoretical lens in the current study is the Technology Acceptance Model (TAM), which argues that technology use depends on perceived use and perceived usefulness. The results revealed that digital marketing is essential for SME effectiveness, as a driver of digital transformation, leading to stronger economic results and an enlarged market presence. Engagement with customers on digital channels to understand their behavior and preferences helps organizations create better experiences and products, and sell smarter as well. This offers application insights to the practitioners and proves the empirical data telling the SMEs how to plan the adoption of digital marketing. It also offers policy recommendations to authorities on how to promote digital innovation and provide financial and technical assistance to SMEs in this vital area. Full article

The development of geographic information systems has grown significantly over the past decade. Simultaneously, the concept of smart cities based on the management of large volumes of data has also spread worldwide. The digital twin concept has recently been incorporated into the technological domain of urban management. However, currently, phases such as technological integration, standardization, data and process interconnection, the development of tools and utilities, professional training, and the application of digital urban development in real-world situations are converging. This paper presents the experience developed on a university campus, detailing each of the phases carried out from the initial design to a fully operational pilot phase model. The article highlights the importance of certain aspects to consider in each phase, demonstrating that there are barriers and limitations and at the same time, great strengths and opportunities in applying the digital twin model in urban management, considering aspects such as mobility, accessibility, energy management, and involving students and university administrators in the process. Full article

Community seed banks are farmer-managed organizations that conserve and sustainably manage local crop and tree diversity. They are found in many countries of the Global South and increasingly in the Global North. Altogether, they maintain hundreds of crop and tree species and thousands of mostly local varieties and distribute tons of quality seed per year. Through their activities, they share and safeguard the world’s agrobiodiversity, contribute to seed security, and allow farming households in local communities to produce and consume more affordable, secure, diverse, and nutritious foods. However, community seed banks are knowledge-, resource-, and time-intensive organizations that operate through their members’ voluntary contributions. The purpose of this article is to analyze the sustainability challenge of community seed banks and identify strategies that address it. Focus group discussions and key informant interviews were used, complemented by secondary data analysis of research reports and other deliverables, resulting in five case study countries in Africa and Asia. Five promising sustainability strategies can support viable community seed bank development: value addition; nature-positive agriculture; enabling environment and national genebank partnership; networking and digitalization; and modern, low-cost seed quality technologies. Sustainable community seed banks can make important contributions to national seed sector development but they need stronger policy and legal support to maintain their sustainability. Full article

Heavy rainfall is the main factor inducing the failure of loess slopes. However, the failure mechanism and mode of terraced loess slopes under heavy rainfall have not been well investigated and understood. This paper presents the experimental study on the deformation and failure of terraced loess slopes with different gradients under extreme rainfall conditions. The deformation and failure processes of the slope and the migration of the wetting front within the slope during rainfall were captured by the digital cameras installed on the top and side of the test box. In addition, the mechanical and hydrological responses of the slope, including earth pressure, water content, pore water pressure, and matric suction, were monitored and analyzed under rainfall infiltration and erosion. The experimental study shows that the deformation and failure of terraced loess slopes under heavy rainfall conditions exhibit the characteristic of progressive erosion damage. In general, the steeper the slope, the more severe the deformation and failure, and the shorter the time required for erosion failure. The data obtained from sensors embedded in the slope can reflect the mechanical and hydraulic characteristics of the slope in response to rainfall. The earth pressure and pore water pressure in the slope exhibit a fluctuating pattern with continued rainfall. The failure mode of terraced loess slopes under extreme rainfall can be summarized into five stages: erosion of slope surface and formation of small gullies and cracks, expansion of gullies and cracks along the slope surface, widening and deepening of gullies, local collapse and flow-slip of the slope, and large-scale collapse of the slope. The findings can provide preliminary data references for researchers to better understand the failure characteristics of terraced loess slopes under extreme rainfall and to further validate the results of numerical simulations and analytical solutions. Full article

To enhance the digital competencies of pharmacy students, the Institute of Clinical Pharmacy and Pharmacotherapy at Heinrich-Heine University Duesseldorf developed and evaluated a theoretical module on digital health and data analysis. This innovative module integrated a continuous glucose-monitoring (CGM) wearable device into teaching, providing students with in-depth practical experience and a 2.5 h seminar on digital health and CGM systems. Students’ knowledge of CGM and self-assessment of their CGM competencies were assessed in a pre-post manner. Additionally, students’ satisfaction with the module and their perceptions of the future integration of digital health training and the role of wearables in pharmacy practice were also assessed after the module. A total of 39 final-year pharmacy students completed the module conducted in June 2024 as part of the clinical pharmacy seminar. In total, 32 students completed the pre- and post-knowledge tests and self-assessment questionnaires. Both the knowledge and the students’ self-assessment of CGM-related skills after the module increased significantly (p < 0.05). Students expanded their knowledge regarding digital health solutions, in particular the CGM systems, and increased their self-reported competence in CGM-related skills. With this module, an important foundation was laid, as this is the first theoretical module including the essentials of CGM digital health tools for pharmacy students in Germany. Full article

Following the COVID-19 pandemic crisis, the emphasis on digitization and robotization has grown at an unprecedented rate in the global economy, resulting in significant changes to the labour market composition and increasing the value of digital skills. The aim of this article is to emphasize the ways in which people’s digital abilities and appetite for online activities are connected to job productivity (salary levels) and to determine which individual internet-based digital skills are genuinely important and correlated with better wages. We employed a Principal Component Analysis (PCA-type factorial analysis) with orthogonal rotation to gain a general understanding of the main components that synthesize the digital capabilities of individuals from the European countries analyzed. We decreased the dimensionality of our initial dataset to two major components, namely comprehensive online skills and digital social and media skills, keeping more than 80% of the overall variability. We then evaluated the potential association between the two created components and the average hourly wages and salaries. Since the end of the COVID-19 pandemic, we have observed an important shift in the impact of digital and internet skills on the job market in Europe. Thus, the development of comprehensive internet skills is highly correlated with individuals’ more effective integration into the labour market in Europe in general and the EU in particular, evidenced by better wage and salary levels (r = 0.740, p < 0.001). On the other hand, we found no correlation between the possibility of obtaining higher salaries for employees and the second component, digital social and media skills. The novelty of our research lies in its specific focus on the unique and immediate impacts of the pandemic, the accelerated adoption of digital skills, the integration of comprehensive individual internet skills, and the use of the most recent data to understand the labour market’s characteristics. This new approach offers fresh insights into how Europe’s workforce could evolve in response to unprecedented challenges, making it distinct from previous studies of labour market skills. Full article