Search Results (1,254)

Background/Objectives: Healthcare organizations worldwide face challenges in retaining talented employees, with the phenomenon of quiet quitting increasingly recognized as a contemporary issue. Rather than leaving their jobs, employees remain at work but exert minimal effort and exhibit reduced engagement, which can ultimately undermine the performance of healthcare organizations. The aim of this research was to examine the impact of servant leadership on work engagement within healthcare organizations, to determine whether this leadership style can help mitigate the effects of quiet quitting. Methods: The study employed a quantitative approach, utilizing validated instruments to measure servant leadership and work engagement. A cross-sectional study design was utilized, employing a convenience sampling method. A total of 362 valid surveys were collected from healthcare professionals in Serbia participating in the study from January to March 2025. The partial least squares structural equation modeling (PLS-SEM) method was used to examine the relationship between servant leadership and work engagement among healthcare professionals. Results: The results indicate that servant leadership has a positive and statistically significant impact on all dimensions of engagement: vigor, dedication, and absorption. Conclusions: Based on these findings, it is concluded that servant leadership can serve as an effective strategy for enhancing work engagement and reducing negative employee behaviors, such as quiet quitting, which may, in turn, improve organizational efficiency in the healthcare industry. Full article

Objective: Vitamin D deficiency (VDD) is common in pregnancy and may affect lipid metabolism. The underlying mechanisms are multifactorial, but most evidence so far comes from non-pregnant populations. This study aims to identify metabolites and metabolic patterns associated with VDD in early pregnancy and to evaluate their relationships with maternal lipid profiles. Methods: A nested case–control research was carried out in the Zhoushan Pregnant Women Cohort (ZPWC). Cases were defined as women with VDD (25(OH)D < 20 ng/mL), and controls (≥20 ng/mL) were matched 1:1 using propensity scores based on age, pre-pregnancy BMI, gestational week, and calendar year at blood sampling. The untargeted metabolomics of first-trimester maternal plasma were measured. Metabolic profiles were analyzed using partial least squares-discriminant analysis (PLS-DA). Principal component analysis (PCA) was applied to visualize group separation, and metabolite set enrichment analysis (MSEA) was performed to reveal biologically relevant metabolic patterns. Associations between VDD-related metabolite components in early pregnancy and lipid levels in mid-pregnancy were assessed using linear regression models. Results: 44 cases and 44 controls were selected for the study. There were 60 metabolites identified as being connected to VDD. Among these, 26 metabolites, primarily glycerophospholipids and fatty acyls, exhibited decreased levels in the VDD group. In contrast, 34 metabolites showed increased levels, mainly comprising benzene derivatives, carboxylic acids, and organooxygen compounds. PCA based on these metabolites explained 52.8% of the total variance (R²X = 0.528) across the first six principal components (PC1: 16.4%, PC2: 10.6%, PC3: 9.2%, PC4: 6.3%, PC5: 5.7%, PC6: 4.6%). PC2, dominated by lineolic acids and derivatives, was negatively associated with total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C) (all p < 0.01). PC3, dominated by glycerophosphocholines, was negatively associated with TC, TG, and high-density lipoprotein cholesterol (HDL-C) (all p < 0.05). MSEA revealed significant enrichment of the pantothenate and CoA biosynthesis pathway after multiple testing correction (FDR < 0.05). Conclusions: This study reveals distinct metabolic alterations linked to VDD and suggests potential mechanisms underlying its association with maternal lipid metabolism in early pregnancy. Full article

As the demand for biodiesel continues to rise, there is a pressing need for efficient and continuous monitoring of the transesterification reaction at the industrial level. However, there is a lack of straightforward online monitoring methods capable of accurately following the course of ethanolysis under various reaction conditions. In this work, simple linear regression (SLR) and multiple linear regression (MLR) models were developed to assess Fourier transform infrared spectroscopy (FTIR) data from a continuous flow cell, enabling real-time ethanolysis monitoring without sample pretreatment. Gas chromatography (GC) was utilised as the reference method to accurately characterise the reaction mixture’s composition during ethanolysis. Extensive correlation analysis was performed to identify spectra regions where the reaction system’s state changes are observable. The gained regions were subsequently applied in the linear regression model’s development. This novel approach resulted in the performance of simple linear regression comparable to complex partial least squares (PLS) regression model (RMSEP = 2.11). The developed online monitoring system was validated in a wide range of reaction conditions (40–60 °C; 0.25–1.0% w/w NaOH); it effectively identifies dynamic changes in the ethanolysis process and confirms achieving the threshold value of ester content set by EU regulation directly in the production process. Full article

The identification of estrus at the right moment allows for a higher success of fecundity with artificial insemination. Evaluating changes in body surface temperature of sows during the estrus period using an infrared thermography camera (ITC) can provide an accurate model to predict these changes. This pilot study comprised nine crossbred Large White x Landrace sows, providing 59 data records for analysis. Observed changes in the behavior and physiological signs of the sows signaled the identification of estrus. Images of the ocular area, ear tips, breast, back, vulva, and perianal area were collected with the ITC. The images were analyzed using the FLIR Thermal Studio Starter software. Infrared mean temperatures were reported and compared using ANOVA and Tukey–Kramer tests (p < 0.05). Supervised machine learning models were tested using random forest (RF), Conditional inference trees (Ctree), Partial least squares (PLS), and K-nearest neighbors (KNN), and the method performance was measured using a confusion matrix. The orbital region showed significant differences between estrus and non-estrus states in sows. In the confusion matrix, the algorithm predicted estrus with 87% accuracy in the test set, which contained 40% of the data, when agitated behavior was combined with orbital area temperature. These findings suggest the potential for integrating behavioral and physiological observations with orbital thermography and machine learning to detect estrus in sows under field conditions accurately. Full article

Background: Despite growing interest in supply chain resilience (SCRes), theoretical overlap between dynamic capabilities (DC) and supply chain agility (SCA) has complicated empirical analysis of their distinct roles. Additionally, the contextual role of information asymmetry in shaping resilience remains underexplored. This study addresses both issues by modeling DC hierarchically and examining IA as a moderator. Methods: Data were collected through a cross-sectional survey of 157 U.S.-based supply chain professionals. Partial least squares structural equation modeling (PLS-SEM) was used to examine the relationships among DC, SCA, IA, and SCRes. Results: SCA was a strong, direct predictor of SCRes. In contrast, DC showed no direct effect in the full model; however, in a hierarchical component model (HCM), DC, a higher-order construct, emerged as significant predictor of SCRes. IA exerted a dual negative influence: it directly weakened SCRes and negatively moderated the relationship between DC and SCRes. Conclusions: This study makes two novel contributions. First, it resolves ambiguity between DC and SCA by empirically modeling DC as a higher-order construct that encompasses but remains distinct from SCA. Second, it introduces IA as a multidimensional barrier to resilience, demonstrating its direct and interactive effects. These findings provide new insight into capability design and contextual adaptation for SCRes in uncertain, information-constrained environments. Full article

Background: In an increasingly digital marketplace, social media marketing activities (SMMAs) have become vital for building consumer–brand relationships, particularly among Generation Z (Gen Z) consumers. Coffee shops offer a unique context because they are lifestyle-oriented and highly dependent on repeat visits, making them especially responsive to digital engagement. This study examines the impact of SMMAs on brand loyalty in the Saudi Arabian coffee shop sector, with a particular focus on the mediating role of consumer brand preference and the moderating role of brand awareness. Drawing on the Stimulus–Organism–Response (S-O-R) framework and Generational Marketing Theory, the research explores how Gen Z consumers respond to social media efforts that are informative, interactive, trendy, and personalized. Methods: Data were collected using convenience sampling via an online survey of 412 Gen Z consumers in Saudi Arabia who follow at least one local or international coffee shop brand on social media. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to test the hypothesized relationships, mediation, and moderation effects. Results: The findings revealed that SMMAs have a substantial direct effect on both brand loyalty and consumer brand preference. Moreover, consumer brand preference partially mediates the relationship between SMMAs and brand loyalty, underscoring its importance as a psychological mechanism in the formation of loyalty. Additionally, brand awareness was found to significantly moderate the SMMAs–brand loyalty relationship, with more potent effects observed among consumers with higher levels of brand familiarity. Implications: The study contributes theoretically by extending the S-O-R framework with Generational Marketing Theory, demonstrating the partial mediating role of brand preference and the moderating direct effect of brand awareness. Practically, the results suggest that coffee shop marketers should design social media strategies that are informative, interactive, trendy, and personalized while also investing in awareness-building campaigns to amplify loyalty among Gen Z consumers. Full article

Purpose: The purpose of this paper is to explore how perceptions of digital health transformation play a role in Saudi Arabia’s customer engagement in healthcare, according to Vision 2030. Saudi Vision 2030, a national reform agenda, has prioritized healthcare digitalization to enhance efficiency, access, and patient-centered care. In particular, the research attempts to explore the attitude of the patient and whether cultural values and infrastructure issues play a mediator role in the perception–engagement relationship. Design/methodology/approach: The study used a mixed-method approach, with qualitative interviews from providers and consumers, along with survey responses from 402 users of digital health. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to examine hypothesized relationships and moderation effects. Findings: Findings establish that digital health perceptions are a crucial driver in enhancing engagement (β = 0.386; p < 0.001). Perceived ease of use (β = 0.368) and usefulness (β = 0.530) exhibited strong positive influences. Moderation analysis revealed that cultural values (β = 0.343) and infrastructure (β = 0.253) further enhance engagement. The findings highlight usability, usefulness, and context as foundational enablers of long-term patient engagement. Originality/value: By combining Technology Acceptance Model (TAM) variables and applying cultural and infrastructural moderators, this research provides new empirical evidence of Saudi Arabian digital health adoption. It provides policy and practical advice in the creation of accessible, culturally appropriate, and adequately supported digital health solutions toward Vision 2030. It also supports United Nations Sustainable Development Goals (SDGs). The study aligns with SDG 3 (Good Health and Well-Being), SDG 9 (Industry, Innovation and Infrastructure), SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action) by further promoting sustainable healthcare transformation in a global development agenda. Full article

Plastic pollution from polyethylene-based materials is a critical environmental concern due to their high persistence. Here, we report the first proof-of-concept application of a multitechnique analytical framework for quantifying linear low-density polyethylene (LLDPE) in Tenebrio molitor frass. Artificially enriched frass–LLDPE mixtures were analyzed using thermogravimetric analysis (TGA), TGA coupled with Fourier-Transform Infrared Spectroscopy (FTIR) and Mass Spectrometry (MS), TGA under inert atmosphere, and solid-state ¹³C nuclear magnetic resonance spectroscopy with Cross-Polarization and Magic Angle Spinning (CP-MAS NMR) ¹³C CP-MAS NMR combined with interval Partial Least Squares (iPLS) modeling. Thermal methods provided insight into decomposition pathways but showed reduced specificity at <1% w/w due to matrix interference. CP-MAS NMR offered matrix-independent quantification, with characteristic signals in the 10–45 ppm region and a calculated LOD and LOQ of 0.173% and 0.525% w/w, respectively. The LOQ lies within the reported ingestion range for T. molitor (0.8–3.2% w/w in frass), confirming biological relevance. This validated workflow establishes CP-MAS NMR as the most robust tool for quantifying polyethylene residues in complex matrices and provides a foundation for in vivo biodegradation studies and environmental monitoring. Full article

The focus of this research is Gen Z, aiming to fill a research gap by determining the key empowering factors that motivate Gen Z to participate in tourism. The research employed a quantitative approach, utilizing a structured questionnaire-based survey method and descriptive statistics, Exploratory Factor Analysis (EFA), and Partial Least Squares Structural Equation Modeling (PLS-SEM). The study was conducted on students as an appropriate convenience sample since most students belong to the Gen Z population. By implementing Exploratory Factor Analysis (EFA) on the Resident Empowerment through Tourism Scale (RETS 2.0), a modified four-factor structure was identified and adapted for measuring empowerment for Gen Z (ZRETS—Gen Z as Residents’ Empowerment through Tourism Scale) as a validated instrument. Identified ZRETS’s (Gen Z as Residents’ Empowerment through Tourism Scale) empowering factors are related to participation within an integral ZRETP model (Gen Z Residents Empowerment through Tourism for Participation). PLS-SEM was employed to examine the structural relationships between empowerment and participation within the ZRETP model (Gen Z Residents Empowerment through Tourism for Participation). The main findings indicated socio-psychological empowerment as a key theoretical base for understanding Gen Z’s participation in tourism. For environmental empowerment and economic empowerment, their mediating role was confirmed, where environmental empowerment is a key mediator and economic empowerment is a result of socio-psychological empowerment effects. Political empowerment was found irrelevant and was excluded from the model, indicating the need for creating a new approach for engaging Gen Z in participation and alternative participation channels more oriented on digital media. The main paper contribution represents the validated ZRETP model (Gen Z Residents Empowerment through Tourism for Participation), as an integrated empowerment–participation framework tailored for Gen Z. Theoretically confirmed importance of Gen Z’s internal empowerment (evident from socio-psychological empowerment effects) and environmental values (evident from environmental empowerment effects) in creating benefits (evident from economic empowerment effects) can guide practitioners in creating sustainable tourism strategies, emphasizing socio-psychological and environmental empowerment, which motivate Gen Z to participate and lead to economic prosperity. Limitations in this paper are related to the convenience sample of students and the PLS-SEM that is used in validating the ZRETP model (Gen Z as Residents’ Empowerment through Tourism Scale). Full article

Heat stress has a major impact on dairy cow health and productivity, especially during early lactation. Conventional heat stress monitoring methods frequently rely on single indicators, such as the temperature–humidity index (THI), which may miss subtle physiological and metabolic responses. This study presents a novel threshold-based classification framework that integrates biologically meaningful combinations of environmental, behavioural, and physiological variables to detect early-stage heat stress responses in dairy cows. Six composite heat stress conditions (C1–C6) were developed using real-time THI, milk temperature, reticulorumen pH, rumination time, milk lactose, and milk fat-to-protein ratio. The study applied and assessed five supervised machine learning models (Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Random Forest (RF0, Neural Network (NN), and an Ensemble approach) trained on daily datasets gathered from early-lactation dairy cows fitted with intraruminal boluses and monitored through milking parlour sensor systems. The dataset comprised approximately 36,000 matched records from 200 cows monitored over 60 days. The highest classification performance was observed for RF and NN models, particularly under C1 (THI > 73 and milk temperature > 38.6 °C) and C6 (THI > 74 and milk temperature > 38.7 °C), with AUC values exceeding 0.90. SHAP analysis revealed that milk temperature, THI, rumination time, and milk lactose were the most informative features across conditions. This integrative approach enhances precision livestock monitoring by enabling individualised heat stress risk classification well before clinical or production-level consequences emerge. Full article

This study examines how Batik Ciprat functions as a vehicle for inclusive creative tourism in Karangpatihan Village, Indonesia, whereby individuals with intellectual disabilities contribute to a socially impactful rural tourism model. Grounded in social capital theory, we analyse how trust, social networks, and inclusive norms enable empowerment and help establish a distinctive, inclusion-oriented tourism identity. Using a qualitatively led mixed-methods (sequential exploratory) design with partial least squares-structural equation modelling (PLS-SEM), we identify key pathways through which art-based entrepreneurship supports village branding and visitor engagement. Our findings indicate that when social innovation is embedded in cultural tourism, it catalyses local pride, participation, and quality of life gains. The study contributes to debates on inclusive/accessible tourism, demonstrating that creative industries can act as engines of equitable, community-based development in rural settings. Full article

This study investigates the influence of FOMO, loss aversion, and herd behavior on gold investment decisions in the Vietnamese market. Employing data collected from 727 investors and the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, the analysis results confirm the pivotal role of FOMO, with both direct and indirect impacts on gold investment decisions. Notably, both loss aversion and herd behavior positively influence FOMO, thereby indirectly encouraging relatively hasty and inadequately considered investment decisions. The study also finds that FOMO has a negative relationship with anticipated regret but is positively correlated with subjective expected pleasure. Furthermore, as determined through Multi-Group Analysis (MGA), psychological messages featuring “self-decision” or “risk warning” demonstrate a significant moderating role, potentially reducing or enhancing the influence of FOMO on investment decisions. These findings contribute to enriching behavioral finance theory and provide an empirical basis for developing effective risk management policies and gold market regulation aimed at mitigating the negative impacts of FOMO. Full article

Avocado is a unique fruit in which of seven-carbon (C7) sugars (notably D-mannoheptulose and perseitol) dominate the carbohydrate profile at harvest. Despite growing interest in sugar-mediated ripening processes, limited comparative data exist across cultivars. This work characterises the dynamic changes in non-structural carbohydrates in the mesotecarp of three commercially relevant avocado varieties—Bacon, Fuerte, and Hass—across four defined ripening stages, from unripe to overripe, with five biological replicates per stage. Using a validated hydrophilic interaction liquid chromatography–mass spectrometry (HILIC–MS) method, we quantified five key sugars and assessed their evolution through ripening. Concentrations varied among the studied samples within the following ranges: D-mannoheptulose, 0.4–49 mg/g dry weight (DW); perseitol, 0.5–23 mg/g DW; glucose, 0.8–5.3 mg/g DW; fructose, 0.6–4.5 mg/g DW; and sucrose, 0.5–3.4 mg/g DW. C7 sugar levels consistently declined, while C6 sugars increased—primarily between the intermediate and ready-to-eat stages—with distinct cultivar-specific patterns. Bacon maintained elevated C7 concentrations for a longer period; Fuerte exhibited a rapid transition from C7 to C6 sugars; and Hass displayed a more gradual and balanced shift. Multivariate analysis (partial least squares discriminant analysis, PLS-DA) effectively discriminated between cultivars at each ripening stage, confirming cultivar-specific metabolic signatures. These findings offer new insights into avocado carbohydrate metabolism, emphasising variety-dependent pathways that could inform breeding strategies, optimise postharvest ripening protocols, and support the nutritional characterisation of different avocado cultivars. Full article

Background: Stunting remains a major public health issue globally and in Indonesia, often linked to inadequate complementary feeding, cultural practices, and limited family empowerment. Objective: This study aimed to develop and evaluate a family empowerment model based on transcultural care theory to improve quality and prevent stunting among children aged 6–24 months. Methods: A cross-sectional explanatory survey was conducted among 324 mother–child pairs from 11 primary healthcare centers in Kediri, East Java. Data were collected using a validated questionnaire covering demographic, educational, technological, economic, and cultural factors, as well as family empowerment and quality. Structural Equation Modeling with Partial Least Squares (SEM-PLS) was applied for hypothesis testing and model development. Results: The model showed moderate explanatory power (R² = 0.223 for family empowerment; R² = 0.115 for complementary feeding quality). Demographic, educational, technological, economic, and cultural factors significantly influenced family empowerment (p < 0.05), which in turn had a strong positive effect on quality (β = 0.340, p < 0.001). Family empowerment mediated the relationship between these factors and quality. Key contributors included knowledge, technology access, income level, and cultural practices. Conclusions: The proposed transcultural care-based family empowerment model effectively improves quality. Strengthening health education, supporting community health volunteers, and integrating culturally sensitive practices, such as encouraging paternal involvement and shared meals, should be prioritized in stunting prevention programs. The model may be adapted for use in similar community settings to enhance program effectiveness. Full article

Leadership literature suggests that a transformational leadership style can reduce negative employee outcomes, even in challenging work environments such as the education sector, where teachers play a key role in social development. This study aimed to analyze the association between transformational leadership and job satisfaction: the role of gratitude toward the organization in the Peruvian context. A cross-sectional study with an explanatory design was conducted considering 457 men and women who declared themselves teachers, aged between 18 and 73 years (M = 38.63; SD = 10.61), recruited through non-probability convenience sampling. The theoretical model was evaluated using the Partial Least Squares method (PLS-SEM). An adequately fitting measurement model was obtained (α = between 0.893 and 0.969; CR = between 0.897 and 0.971; AVE = between 0.757 and 0.845), demonstrating that transformational leadership is positively associated with the components of gratitude toward the organization and job satisfaction, as well as the association of the components of gratitude toward the organization and job satisfaction. In turn, it was evident how gratitude toward the organization plays a mediating role in these relationships. In this sense, the study provides valuable information for Peruvian educational leaders seeking to improve indicators of satisfaction, gratitude, and leadership in their work environment. These findings enrich educational management, given that it is the first empirical study to demonstrate these links in a challenging sector of an emerging country, offering a solid foundation for the development of more humanized, effective, and sustainable management strategies. Full article