Search Results (658)

Chronic diseases drive healthcare costs, and employers seek scalable strategies to improve health outcomes and control expenses. Telehealth behavioral care shows promise for managing chronic conditions, but its long-term economic value in employer populations is still unclear. We assessed the cost-effectiveness and ROI of a behavioral care (LBC) model using a Markov model in a custom analytic tool. The model simulated disease progression, healthcare utilization, and QALYs over five years from the employer perspective. Transition probabilities, costs, and mortality risks were obtained from the InHealth program, national sources, and published literature. Employees in the behavioral care model were compared with a control group receiving usual care. Among 4461 employees aged 40, intervention participants had five-year costs of $41,431, versus $47,834 for controls, saving $6403 per member and $28.6 million overall. Treated members gained 4.7 QALYs compared to 4.6 in controls, equivalent to 36.5 extra days of full health. The program had a ROI of 6.53, showing significant cost savings. Telehealth behavioral care is a cost-effective way to improve health outcomes and provide financial benefits to employers. These results support incorporating behavioral care into value-based benefits and highlight potential long-term savings through prevention and management of lifestyle-related chronic diseases. Full article

Social media platforms have become critical infrastructures for customer relationship management (CRM), requiring scalable and intelligent solutions to handle high-volume interactions. In the luxury fragrance sector, digital promotion poses a unique challenge because olfactory attributes cannot be experienced online. As a result, physical fragrance samples remain essential, generating large volumes of sample requests or inquiries across social media. However, many requests remain unmanaged due to limitations in manual CRM (i.e., human-driven processes), revealing a design gap that may negatively affect perceived responsiveness and service quality. This study uses qualitative content analysis with NVivo 12 to examine large-scale sample request interactions on the Facebook pages of four luxury fragrance brands. Data was collected via NCapture and analyzed to identify recurring patterns, linguistic structures, and customer expressions related to sample requests. Findings confirm frequent repetitive requests, highlighting inefficiencies in traditional CRM systems under high demand. This research proposes an AI-driven CRM Sample Request Management Architecture (CRM–SRMA) that systematically captures and processes customer sample requests, collects the necessary mailing information, and seamlessly transfers validated data to the final dispatching stage. The proposed system also models individual fragrance preferences by analyzing customers’ interactions with samples, particularly in terms of top, middle, and base notes. By leveraging this information, the architecture enables the targeted promotion of new fragrance releases that closely align with customers’ demonstrated olfactory preferences. The insights of this research provide a scalable, intelligent mechanism that enables luxury social media managers and CRM systems to manage high-volume interactions while maintaining service quality. By automating sample request processing, the mechanism improves responsiveness and reduces operational burden. It also supports long-term relationship building through preference tracking and updating customers with any new relevant-fragrance releases. Although focused on fragrances, the mechanism is adaptable to other luxury cosmetic categories, thereby ideally enhancing overall social media-based customer service. Full article

Today’s global and competitive environment forces companies to revise their competitive strategies and assess their operations’ performance. Customers are demanding new products and services, and organizations should adapt to the changing requirements of the customers. Companies may achieve excellence in their operations with cost reduction, by reducing time-to-market, and through improvements in delivery and quality. The main contribution of this study is assessing the linkages among operational performance (OP), environmental dynamism (ED), and competitive strategy (CS) in an emerging economy, Turkey. This study also aims to define the dimensions used to assess the operational performance, which are called the competitive manufacturing priorities in the operations management literature. To test the linkages between environmental dynamism, operational performance, and competitive strategy, a structural model is proposed. Analyses are conducted in SPSS 28.0 and AMOS 24.0 programs using the data gathered from Turkish manufacturing companies. Since 99.8% of firms operating in Türkiye are SMEs, most of the companies participating in this study (124 of 211) are also SMEs, and another contribution of this study is understanding the dimensions affecting the operational performance of SMEs According to the results, environmental dynamism has a significant relation to operational performance, and operational performance has a positive linkage with competitive strategy as well. The results also indicate that the most important dimensions used in assessing operational performance are customer satisfaction and supplier performance, as expected for manufacturing companies. Furthermore, the results of this study are expected to support organizations in developing and implementing effective strategies that integrate new capabilities and environmental considerations into their competitive strategy. As expected in SMEs, the most used competitive strategy is found to be “cost leadership,” because they can achieve operational performance by efficiently using resources, and by minimizing the production and transaction costs, they can enhance their competitiveness in the market. Full article

Background and Objectives: Immunohistochemistry (IHC) is a keystone in gastric cancer (GC) management, allowing treatment customization, including for advanced or metastatic diseases. This review aims to evaluate the critical role of IHC markers, analyzing their efficiency in molecular subclassification and prediction of response to gastric cancer-targeted therapies, while also describing state-of-the-art IHC techniques and perspectives. Results: The major challenges for the GC management were structured in two main sections, as follows: (i) the current paradigm of gastric neoplasia diagnosis, which includes subsections related to the methodological and morphological foundations, the epidemiological dynamics, and risk factors, as well as differential diagnosis of poorly differentiated tumors; and (ii) the progress in 3,3′-diaminobenzidine (DAB) application and advanced reagents in gastric cancer immunohistochemistry. Discussion: Considering the role of IHC and DAB, the following topics were successively addressed in seven sections: GC key biomarkers, such as human epidermal growth factor receptor 2 (HER2), programmed death-ligand 1 (PD-L1), and DNA replication mismatch repair (MMR) system, allow direct correlation between tissue morphology and protein expression; intestinal and gastrointestinal differentiation markers; emerging and aggressive histological subtypes; epithelial–mesenchymal transition, E-cadherin, and the process of tumor budding; implementation of innovative procedures in gastric cancer immunohistochemistry; and automation, quality control, and sustainability in the pathology laboratory. Perspectives: The main directions were focused on the integration of artificial intelligence (AI) algorithms for digital quantification of the IHC signal and also on the expansion of panels to new targets, such as Claudin 18.2 (CLDN 18.2), which redefines treatment approaches in advanced stages. Conclusions: Although faced with technical and biological limitations, immunohistochemistry remains indispensable in modern gastric oncology. The evolution towards digital pathology and the refinement of scoring criteria will transform IHC from a complementary test into a visual tool that is essential for personalizing oncological treatment. Full article

Service industries are fast turning digital, and they are disrupting the manner in which firms organize and relate to their customers. Nevertheless, mechanisms that allow digital capabilities to be converted to better customer experience remain poorly understood, especially in the emerging economies. This paper presents the dual-mediation model to investigate the effects of Digital Supply Chain Integration (DSCI) and capabilities of digital customer engagement (DCEC) on Customer Experience Outcomes (CXO) based on Sustainable Business Model Innovation (SBMI) and Customer Value Co-Creation (CVC) with references to Dynamic Capabilities Theory and Service-Dominant Logic. The data were collected by a cross-sectional survey of 360 managers of the banking, telecom, healthcare, and hospitality organizations in Pakistan and analyzed with the help of Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that the customer experience is enhanced well beyond what is feasible alone by means of Sustainable Business Model Innovation due to the Digital Supply Chain Integration, whereas customer experience is enhanced by the Digital Customer Engagement Capabilities through Customer Value Co-Creation. The results also show that the mechanisms of co-creation have the largest impact on the outcome of customer experience. The research is an addition to the body of literature on digital transformation because it illustrates how digital integration, innovation that is focused on sustainability, and relational co-creation can collectively convert digital capabilities into experiential value. The results also provide practical factors that should be considered by service companies when embarking on digital transformation programs to align the programs with sustainability and customer engagement mechanisms. Full article

Ports play a central role in global trade and act as key hubs for both maritime and land transport. Free ports, characterized by special customs regimes and fiscal advantages, represent a distinctive segment of this landscape. Despite their relevance, the literature on port gate management and on free ports has developed disconnected research streams, leaving the operational implications of special customs regimes largely unexplored. This study addresses this gap by investigating how gate procedures in free ports can be managed more efficiently, using the Port of Trieste as a case study. The analysis combines Business Process Model and Notation (BPMN) with discrete event simulation: BPMN served as the logical foundation for capturing the procedural complexity of free port gate operations, while simulation provided the quantitative framework for scenario evaluation. The model was calibrated on real gate access data and validated against observed vehicle volumes. Nine scenarios were evaluated, covering managerial, technological, infrastructural, and disruption-related interventions. The results show that no single measure produces significant improvements across all performance indicators and the integrated approaches consistently outperform standalone measures. Infrastructure interventions, while more costly, prove particularly valuable in improving port resilience under severe disruption conditions. Full article

The construction sector is one of the primary areas that underpin a country’s economic development. However, this sector is characterized by various types of obstacles, including the participation of numerous stakeholders, strict schedules, limited resources, and the management of vast amounts of data throughout the project lifecycle. Building Information Modeling (BIM) has emerged as a promising technology for centralizing and managing construction data throughout the project lifecycle. However, having the ability to extract real-time, decision-oriented insights from BIM models remains a challenge for project stakeholders. To address this limitation, this research paper explores the integration of BIM with Business Intelligence (BI) to enhance control and management of construction projects throughout the development of a customized Power BI dashboard. The proposed framework of the paper utilizes BIM’s data-rich environment and Power BI’s advanced analytical and visualization capabilities to deliver real-time and interactive insights about project performance and progress. The customized dashboard enables stakeholders, especially project managers, to monitor key performance indicators of the project that are related to cost and schedule. It also supports progress tracking, early identification of inefficiencies, and data-driven decision-making. To demonstrate the practical application of the proposed framework, a case study was conducted. The results indicate that integrating BIM with BI helps in enhancing project control, improving transparency, and facilitating collaboration between stakeholders through a centralized cloud platform that can be easily accessed through desktop and mobile devices. Full article

Background/Objectives: Head and neck cancer survivors experience many late toxicities following radiation therapy. This study aims to identify symptom clusters of late toxicities and their related factors in head and neck cancer survivors. Methods: A cross-sectional study was conducted with 83 survivors (pharyngeal or laryngeal cancer) who had received radiation therapy at least one year earlier. Nine late toxicities were assessed using the Japanese version of the Patient Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) and a custom questionnaire. Quality of life (QoL) and related factors were evaluated with the European Organization for Research and Treatment of Cancer (EORTC QLQ-C 30), Hospital Anxiety and Depression Scale (HADS), UCLA Loneliness Scale, and Liebowitz Social Anxiety Scale (LSAS). Exploratory factor analyses and multiple regression analyses were performed. Results: All participants reported at least one symptom. Dry mouth (90.4%) and difficulty swallowing (72.3%) were particularly prevalent. Exploratory factor analysis (EFA) identified two symptom clusters (SCs): an oropharyngeal dysfunction cluster (pain, trismus, taste changes, difficulty swallowing, hoarseness) and a dry mouth cluster (dry mouth, sticky saliva). Regression analysis indicated that higher scores in both clusters were significantly associated with lower global QoL (oropharyngeal dysfunction SC: β = −0.427, p < 0.001; dry mouth SC: β = −0.268, p = 0.009). Chemoradiotherapy (CRT) was also significantly associated with higher cluster scores (oropharyngeal dysfunction SC: β = 0.233, p = 0.020; dry mouth SC: β = 0.343, p = 0.001). Conclusions: Late toxicities following radiation therapy include two clusters: oropharyngeal dysfunction cluster and dry mouth cluster. Head and neck cancer survivors with higher SC scores had lower global QoL scores and had undergone CRT. These findings may aid in the assessment and self-management support of head and neck cancer survivors after radiation therapy. Full article

Background: Diabetic foot ulcers (DFUs) represent a major chronic complication of diabetes mellitus, often leading to severe infection, amputation, and reduced quality of life. Among various factors affecting DFUs, plantar pressure plays a pivotal role in ulcer formation and recurrence. Despite growing interest in this domain, few studies have comprehensively evaluated the research landscape concerning plantar pressure in the context of DFUs from a bibliometric perspective. Aim: To conduct a comprehensive bibliometric analysis and visualization of global research trends, hotspots, and collaborative networks in the field of plantar pressure-related diabetic foot studies from 2000 to 2024. Methods: A systematic search was conducted in the Web of Science Core Collection (WoSCC) on 16 February 2025, for articles published between 2000 and 2024 using terms related to “diabetic foot” and “plantar pressure”. A total of 2518 records were retrieved, from which 2110 English-language articles and reviews were included. Bibliometric and visual analyses were performed using Microsoft Excel 2021, VOSviewer (v1.6.20), CiteSpace (v6.4.R1), Charticulator, and Scimago Graphica. Analyses included publication trends, country/institution/author collaborations, journal distributions, keyword co-occurrence and clustering, citation bursts, and reference co-citation networks. Results: A total of 2110 publications were identified, showing an overall increase in annual publication output from 2000 to 2024, with some year-to-year fluctuations. The United States led in publication volume (678 articles), citation frequency, and H-index, followed by the United Kingdom and China. Armstrong, David was the most prolific and also had the highest H-index among the listed authors, while the University of Amsterdam was the leading institution. “Journal of Wound Care” had the highest publication count, whereas “Diabetes Care” ranked first in citation frequency. Keyword analysis revealed major research clusters including “diabetic foot”, “plantar pressure”, “wound healing”, “offloading”, and “negative pressure wound therapy”. Recent trends show an increased focus on microcirculation, regenerative medicine, customized footwear, and wound care technologies. Conclusions: The bibliometric analysis reveals research trends and current hotspots in plantar pressure management for diabetic foot ulcers, with a particular focus on managing plantar pressure through personalized offloading strategies and custom footwear. These findings highlight the practical value of tailoring interventions to individual patient needs, emphasizing the importance of biomechanical factors in ulcer prevention and healing. Full article

Strengthening transparency has emerged as a pivotal issue in promoting the responsible development of artificial intelligence (AI). As the prevailing framework for corporate information disclosure, Environmental, Social, and Governance (ESG) reporting shares an inherent synergy with AI governance; both are rooted in the pursuit of sustainable development and the disclosure of specific matters to investors and broader stakeholders. This study analyzes the status of artificial intelligence (AI) information disclosure in the ESG (Environmental, Social, and Governance) reports of listed companies across the United States, Europe, and China, finding that: (1) ESG reports have emerged as a primary channel for business organizations to disclose AI-related information; (2) significant disparities exist in disclosure levels across four key AI-related domains—development, application, manufacturing, and consumption; and (3) disclosure density varies considerably across E, S, and G dimensions, with the Governance (G) pillar exhibiting the most comprehensive information. Based on an empirical analysis of the ESG-AI disclosure framework, this study proposes an optimization scheme for ESG-AI reporting, clearly defining mandatory ESG-AI disclosure obligations for listed companies and employing the “comply or explain” mechanism to balance corporate transparency with operational efficiency while adhering to the “Double Materiality” principle by disclosing model training energy consumption and ecological impacts under Environmental (E) matters, addressing employment, employee training, marketing labeling, and customer privacy under Social (S) matters, and elaborating on corporate AI strategies, risk management protocols, and governance policies under Governance (G) matters. Regarding procedural safeguards, taking China as a case study, centralized disclosure could be implemented through the National Enterprise Credit Information Publicity System, complemented by an assurance system for listed company reports to enhance the accessibility and accuracy of information disclosure. Full article

Organizational ambidexterity is an essential topic in management research. A growing number of studies argue that organizational ambidexterity is increasingly critical to the sustained competitive advantage of firms. However, there is less research on ambidexterity in new technology-based firms, despite the significant impact it has on local and national economies. The study examined the relationship between organizational ambidexterity and firm performance (non-economic and economic). The sample consists of 102 Colombian new technology-based firms. A latent variable design or structural equation modeling was followed. The statistical method was Partial Least Squares Structural Equation Modelling (PLS-SEM). According to the results, organizational ambidexterity is positively related to both non-economic and economic performance. Organizational ambidexterity explained 10% of the variance of the economic performance and 56% of the variance of the non-economic performance. These findings highlight the importance of organizational ambidexterity to obtain better firm performance, especially non-economic performance related to customer perception, employee satisfaction, and improvement in the quality of products and services in new technology-based firms. Full article

Product waste in grocery supply chains remains a major concern for multiple stakeholders, particularly retailers, due to the direct financial losses it generates and the potential risks it poses to customer health and safety. In this context, digital technologies—especially artificial intelligence (AI)—offer promising opportunities to improve retail performance and reduce waste. Accordingly, this study aims to investigate the factors influencing retailers’ intentions to adopt AI-based solutions for product waste reduction. To achieve this objective, the Technology Acceptance Model (TAM) was extended by incorporating three additional constructs (i.e., perceived ethical responsibility, product waste reduction-related knowledge, and perceived economic utility of AI for product waste reduction). Data were collected from a purposive sample of 214 grocery retailers operating in major cities in northern Algeria. Structural Equation Modeling (SEM) was employed to test the proposed research model and hypotheses. The results indicate that retailers’ behavioral intentions to use AI for product waste reduction are significantly influenced by perceived economic utility of AI, AI for product waste reduction-related knowledge, perceived usefulness, and perceived ease of use. In contrast, perceived ethical responsibility for product waste reduction did not exhibit a statistically significant effect, although its relationship with behavioral intention was positive. This study contributes to the growing literature on AI adoption for waste reduction in the retail sector, particularly within developing country contexts, and offers practical insights for policymakers and industry stakeholders seeking to promote the adoption of digital technologies for sustainable supply chain management. Full article

Intervertebral disk pathology, including disk herniation and degeneration, is a major contributor to chronic low back pain, and when conservative treatment fails, surgical management often involves discectomy-based procedures that leave residual annulus fibrosus (AF) defects associated with reherniation and progressive degeneration. These limitations have motivated interest in regenerative strategies using biomaterial scaffolds; however, reproducing the hierarchical, angle-ply architecture of the AF remains challenging. Here, we present a single-step extrusion-based 3D-printing approach to fabricate polycaprolactone (PCL) scaffolds with aligned microscale surface grooves that promote AF-like organization. Patterned nozzles with circumferential peaks generated uniaxial concave microgrooves (10–17 µm wide) directly during printing, enabling formation of multilamellar angle-ply constructs. Human bone marrow-derived mesenchymal stem cells cultured on patterned scaffolds aligned longitudinally within concave grooves, forming end-to-end arrays that guided extracellular matrix deposition. Gene expression analysis showed that topographical cues governed cellular organization without significantly altering gene expression profiles, while TGF-β3 supplementation upregulated outer AF-associated markers, including COL1, COL12, SFRP2, MKX, MCAM, and SCX. TAGLN expression increased specifically on patterned scaffolds in the absence of TGF-β3, indicating an association between microgroove-guided cellular organization and TAGLN expression, warranting further investigation into potential tension-related mechanisms. This novel single-step extrusion-printing approach leverages custom nozzle geometry to impart concave microgrooves, facilitating scalable fabrication of multilamellar angle-ply scaffolds that induce aligned cellular organization and support potential applications in annulus fibrosus repair, as well as mechanobiological studies of anisotropic musculoskeletal tissues. Full article

Operations scheduling of mass customized products is vital in the modern make-to-order (MTO) supply chains. In these systems, order acceptance decisions should be coordinated with available capacity in different sections of the supply chain while considering their potential correlations and interactions. One of the fundamental challenges in optimization of these systems is the computation time of solving models with multiple coupling constraints between supply chain units. This paper addresses this issue by proposing a game-based framework that decomposes the related mixed integer programming mathematical model and it is coordinated and solved using integrated game-based Alternating Direction Method of Multipliers (ADMM). The proposed Stackelberg Leader-Follower game optimizes order acceptance decisions while considering the requirements in supply, production planning, maintenance, inventory, and distribution units. To validate the efficiency of the proposed framework, the model is tested with a simulated four-layer supply chain. The results of experiments proved that decompositions of the model to smaller subsections and solving it in a distributed manner not only optimizes supply chain participating units but also coordinate their movements to achieve the global optimal solution. The proposed framework offers managers a practical decision layer that preserve local autonomy of the supply chain units and reduce their data sharing and computation burdens and concerns. Full article

Industry 5.0 emphasizes human-centered integration of artificial intelligence in industrial contexts, yet successful adoption depends critically on workforce perception and acceptance. This research develops and validates a machine learning framework for predicting AI-related perceptions and expected impacts in the construction industry under small sample constraints typical of specialized industrial surveys. Specifically, the study aims to develop and empirically validate a predictive AI decision support model that estimates the expected impact of AI adoption in the construction sector based on digital competencies, ICT utilization, AI training and experience, and AI usage at both individual and organizational levels, operationalized through a composite AI Impact Index and two process-oriented outcomes (perceived task automation and perceived cost reduction). Using a dataset of 51 survey responses from Slovak construction professionals collected in 2025, we implement a methodologically rigorous approach specifically designed for limited-data regimes. The framework encompasses ordinal target simplification from five to three classes, dimensionality reduction through theoretically grounded composite indices reducing features from 15 to 7, exclusive deployment of low variance regularized models, and leave-one-out cross-validation for unbiased performance estimation. The optimal model (Lasso regression with recursive feature elimination) predicts cost reduction perception with R² = 0.501, MAE = 0.551, and RMSE = 0.709, while six classification targets achieve weighted F1 = 0.681, representing statistically optimal performance given sample constraints and perception measurement variability. Comparative evaluation confirms regularized models outperform high variance alternatives: random forest (R² = 0.412) and gradient boosting (R² = 0.292) exhibit substantially lower generalization performance, empirically validating the bias-variance trade-off rationale. Key methodological contributions include explicit bias-variance optimization preventing overfitting, feature selection via RFE reducing input space to six predictors (personal AI usage, AI impact on budgeting, ICT utilization, AI training, company size, and age), and demonstration that principled statistical approaches achieve meaningful predictions without requiring large-scale datasets or complex architectures. The framework provides a replicable blueprint for perception and impact prediction in data-constrained Industry 5.0 contexts, enabling targeted interventions, including customized training programs, strategic communication prioritization, and resource allocation for change management initiatives aligned with predicted adoption patterns. Full article