Search Results (1,921)

Background/Objectives: End-of-life (EoL) experiences are critically important for everyone involved, giving rise to a set of needs that extend far beyond bio-physiological aspects, to encompass the spiritual dimension as the core of human beings. Understanding the processes of spiritual awakening (SA) assists palliative care professionals in enhancing the quality of care provided to individuals with life-threatening illnesses, as well as to their families. SA is a fundamental occurrence linked to the fulfilment of our spiritual needs when facing an existential crisis, such as the proximity of death. However, its conceptual boundaries need to be clarified to provide qualified and humanized palliative care. Therefore, this study aims to identify the key attributes, antecedents, consequents, and empirical referents of SA at EoL, as well as to clarify the concept’s existing ambiguities. Methods: Walker and Avant’s eight-step concept analysis was used. A literature search was conducted in May 2025 across three databases (PubMed, CINAHL and Scopus). Results: Following the review, 21 articles were included for analysis. The concept analysis revealed four main attribute domains: (1) sensory–perceptual domain; (2) affective/cognitive domain; (3) relational domain; and (4) transcendental domain. Moreover, spiritual consciousness and the existential matrix were antecedents to this concept; revaluation of beliefs, finding spiritual serenity and inner freedom, fostering spiritual growth, and the desire to leave a legacy were its consequences. Conclusions: The concept of SA at the EoL reveals itself to be a complex and multifactorial phenomenon, with a profound impact on a person’s confrontation with finitude. Recognizing and integrating SA into palliative care allows for a more comprehensive understanding of human consciousness. To deal with SA experiences in healthcare settings, a multifaceted approach is needed. This encompasses acknowledging spirituality as a determinant of health, including spiritual care in standard practice, and offering education and training on spiritual care competence for healthcare practitioners. Further transdisciplinary research should be undertaken to explore SA phenomenological variations, guide clinical interventions, and evaluate SA impacts on spiritual well-being and spiritual growth. Full article

Accurately estimating the time of concentration (Tc) is critical for hydrological modelling, flood forecasting, and hydraulic infrastructure design. However, conventional methods often overlook the combined effects of rainfall intensity and antecedent soil moisture, thereby limiting their applicability under changing climates. This study presents a novel approach that integrates data-driven techniques with remote sensing data to improve Tc estimation. This method was successfully applied in the Kalu River Basin, Sri Lanka, demonstrating its performance in a tropical catchment. While an overall inverse relationship between rainfall intensity and Tc was observed, deviations in several events underscored the influence of initial soil moisture conditions on catchment response times. To address this, a modified kinematic wave-based equation incorporating both rainfall intensity and soil moisture was developed and calibrated, achieving high predictive accuracy (calibration: R² = 0.97, RMSE = 1.1 h; validation: R² = 0.96, RMSE = 0.01 h). A hydrological model was developed to assess the impacts of Tc uncertainties on design hydrographs. Results revealed that underestimating Tc led to substantially shorter lag times and significantly increased peak flows, highlighting the sensitivity of flood simulations to Tc variability. This study highlights the need for improved $T_C$ estimation and presents a robust, transferable methodology for enhancing hydrological predictions and climate-resilient infrastructure planning. Full article

Narcissistic Leadership has been associated with negative organizational and individual outcomes, including employee intention to leave. However, the mechanism by which this leadership influences this intention to leave still needs to be further elucidated. This study investigates the mediating role of psychological capital (PsyCap) (comprising hope, self-efficacy, resilience, and optimism) in the relationship between the narcissistic leadership and the intention to leave. A non-probabilistic sample of 266 Portuguese employees from various organizational sectors, aged 18 to 53 (M = 29.13; SD = 7.53), predominantly women (62%), completed a self-administered online questionnaire. Results, calculated through the estimation of OLS regressions-based models, indicated that narcissistic leadership was positively related to turnover intention (Hypothesis 1) and that PsyCap significantly mediated that association (Hypothesis 2), particularly self-efficacy showed to be negatively associated with turnover intention, and optimism positively predicted the intention to leave the organization. Overall, the findings point to the key role of narcissistic leadership and psychological capital as antecedents of turnover intention, highlighting the opposite mediating effects of self-efficacy and optimism in the association between narcissistic leadership and turnover intention. The study’s findings are discussed, as well as their theoretical and practical implications. Full article

Amid ongoing educational reforms and the rapid advancement of the knowledge economy, innovative teaching behaviors are not only closely related to teachers’ professional growth and students’ academic achievement but are also regarded as the key driving force for the evolution of the educational system. Consequently, identifying effective ways to foster teachers’ innovative teaching behaviors has become a central concern in educational psychology and management. Grounded in the Job Demands–Resources framework, this study developed and tested a chained mediation model using survey data from 1165 Chinese elementary and secondary school teachers. The model examines how teachers’ emotional competence fosters innovative teaching behaviors and elucidates the underlying mechanisms. The results revealed that (1) emotional competence significantly and positively predicted innovative teaching behaviors, and (2) teaching efficacy and work vitality served not only as independent mediators but also as sequential mediators in this relationship. These findings extend the understanding of the antecedents of teachers’ innovative behaviors from an emotional perspective, demonstrating that emotional competence, as a critical psychological resource, can be transformed into innovative teaching behaviors through dual “cognitive–motivational” and “energy–motivational” pathways. This study offers both theoretical insights and practical implications for advancing teaching innovation by strengthening teachers’ emotional competence, teaching efficacy, and work vitality. Full article

With the development of generative artificial intelligence (AI) technology, collaboration between humans and AI is expected to improve productivity, efficiency, and safety in various industries. This study presents and empirically analyzes the factors affecting collaboration between humans and AI. This study presents and empirically analyzes a research model based on the antecedents of calculative-based, cognition-based, knowledge-based, and social influence-based trust. A total of 305 valid data points were collected through questionnaires completed by experts, office workers, and graduate students, and were analyzed using structural equation modeling. The analysis showed that all antecedents except familiarity, an antecedent of knowledge-based trust, significantly affected trust. Full article

Functional analysis is a methodology used within the field of Behavioral Analysis to explain, predict, and influence behavior. This is achieved by identifying the functional relationships between behavior, the antecedent stimuli that elicit or evoke behavior, and the consequences of behavior that influence its future occurrence. Within this context, a functional relationship refers to a consistent and observable change in behavior (the “dependent variable”) that results from the systematic manipulation of environmental conditions (the “independent variables”). Functional analyses typically focus on the immediate environmental context, where stimuli functionally related to the behavior are observed. In applied and clinical settings, however, behavior analysts may consider additional variables (e.g., genetic predispositions, social context, learning history) to enhance the accuracy and relevance of their analyses. These variables are usually controlled for or excluded in experimental settings but can play a meaningful role in naturalistic behavior assessment. Full article

Tourism plays a vital role in promoting local economic growth and preserving cultural heritage, with creative cultural tourism increasingly recognized as a strategy for enhancing tourist engagement. This study examines antecedent factors influencing tourist engagement in creative cultural tourism activities at the Tha Plee Fishing Market community, focusing on creative tourism experience, cultural and emotional perception, and travel motivation. The research also evaluates the overall level of tourist engagement and explores the relationships between these factors and engagement. A quantitative research design was employed, with data collected from 400 Thai tourists visiting the community. Descriptive statistics and Structural Equation Modeling (PLS-SEM) were used to analyze the data. The results indicate that all three antecedent factors and overall tourist engagement were rated at a high level. Creative tourism experience had a significant positive effect on tourist engagement (β = 0.286). These findings suggest that immersive, hands-on cultural activities and strong emotional connections to local heritage can enhance engagement. From a practical perspective, community stakeholders and tourism planners should focus on developing unique cultural experiences, improving visitor interaction with local traditions, and promoting storytelling to strengthen emotional bonds. Future research should include international tourists to broaden the generalizability of the results. Full article

This paper reviews the current research landscape on Personal Saving Behavior, focusing on its antecedents and outcomes. Using bibliographic analysis of publication trends—highlighting productive authors, journals, countries, and keywords—the literature is synthesized. A framework-based systematic review is conducted to understand factors influencing saving behavior and its effects, employing the TCM framework to analyze theory, context, and methods across selected studies. Additionally, the ADO framework is used to discuss antecedents, decisions, and outcomes related to personal saving behavior. The review consolidates 112 articles from 2000 to 2025, grouping unique antecedents into nine categories. It also examines how specific antecedents positively or negatively impact saving decisions and outcomes. Finally, using the TCM and ADO frameworks, the study identifies research gaps and discusses future directions, especially from the perspectives of behavioral economics and critical incidents. Full article

Traditional river quality models struggle to accurately predict river water quality in watersheds dominated by non-point source pollution due to computational complexity and uncertain inputs. This study addresses this by developing a novel coupling model integrating a gradient boosting algorithm (Light GBM) and a long short-term memory network (LSTM). The method leverages Light GBM for spatial data characteristics and LSTM for temporal sequence dependencies. Model outputs are reciprocally recalculated as inputs and coupled via linear regression, specifically tackling the lag effects of rainfall runoff and upstream pollutant transport. Applied to predict the concentrations of chemical oxygen demand digested by potassium permanganate index (COD) in South China’s Jiuzhoujiang River basin (characterized by rainfall-driven non-point pollution from agriculture/livestock), the coupled model outperformed individual models, increasing prediction accuracy by 8–12% and stability by 15–40% than conventional models, which means it is a more accurate and broadly applicable method for water quality prediction. Analysis confirmed basin rainfall and upstream water quality as the primary drivers of 5-day water quality variation at the SHJ station, influenced by antecedent conditions within 10–15 days. This highly accurate and stable stack coupling method provides valuable scientific support for regional water management. Full article

As self-service technologies (SSTs) such as kiosks become embedded in service infrastructure, understanding the socio-cognitive drivers of adoption has grown in importance. This study extends the Technology Acceptance Model (TAM) by integrating Digital Readiness (DR), Trust in Technology (TT), Perceived Usefulness (PU), and Perceived Barriers (PB) into a single framework, and tests it using structural equation modeling (SEM) with survey data from 750 kiosk users in China. TT emerges as the strongest direct predictor of intention to use (IU) and also increases PU while reducing PB. The deterrent effect of PB exceeds the positive effect of PU. DR promotes adoption indirectly by raising TT and PU and lowering PB, whereas its direct path to IU is negative, suggesting a tension between readiness and heightened expectations. Multi-group analyses show that non-digital natives and low-frequency users are more sensitive to trust-related factors, whereas digital natives and high-frequency users respond more to barrier reduction. These findings integrate trust and barrier perspectives into TAM and reconceptualize DR as an ambivalent antecedent. Practically, a segment- and journey-oriented design frame centered on trust and friction provides a common reference for aligning kiosk design, KPIs, and investment decisions across industries. Full article

Accurate prediction of system degradation and remaining useful life (RUL) is essential for reliable health monitoring of Lithium-ion (Li-ion) batteries, as well as other dynamic systems. While evolving systems can offer adequate adaptability to the nonstationary and nonlinear behavior of battery degradation, existing methods often face challenges such as uncontrolled rule growth, limited adaptability, and reduced accuracy under noisy conditions. To address these limitations, this paper presents a smart evolving fuzzy predictor with customized firefly optimization (SEFP-FO) to provide a better solution for battery RUL prediction. The proposed SEFP-FO technique introduces two main contributions: (1) An activation- and distance-aware penalization strategy is proposed to govern rule evolution by evaluating the structural relevance of incoming data. This mechanism can control rule growth while maintaining model convergence. (2) A customized firefly algorithm is suggested to optimize the antecedent parameters of newly generated fuzzy rules, thereby enhancing prediction accuracy and improving the predictor’s adaptive capability to time-varying system conditions. The effectiveness of the proposed SEFP-FO technique is first validated by simulation using nonlinear benchmark datasets, which is then applied to Li-ion battery RUL predictions. Full article

This study investigates the adoption of PyChatAI, a bilingual AI-powered chatbot for Python programming education, among female computer science students at Jouf University. Guided by the Technology Acceptance Model 3 (TAM3), it examines the determinants of user acceptance and usage behaviour. A Solomon Four-Group experimental design (N = 300) was used to control pre-test effects and isolate the impact of the intervention. PyChatAI provides interactive problem-solving, code explanations, and topic-based tutorials in English and Arabic. Measurement and structural models were validated via Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM), achieving excellent fit (CFI = 0.980, RMSEA = 0.039). Results show that perceived usefulness (β = 0.446, p < 0.001) and perceived ease of use (β = 0.243, p = 0.005) significantly influence intention to use, which in turn predicts actual usage (β = 0.406, p < 0.001). Trust, facilitating conditions, and hedonic motivation emerged as strong antecedents of ease of use, while social influence and cognitive factors had limited impact. These findings demonstrate that AI-driven bilingual tools can effectively enhance programming engagement in gender-specific, culturally sensitive contexts, offering practical guidance for integrating intelligent tutoring systems into computer science curricula. Full article

The integration of artificial intelligence (AI) in the manufacturing industry is increasingly prevalent, presenting both ongoing opportunities and challenges for organizations while also significantly impacting worker behavior and psychology. Drawing on data from 405 workers in China, this study employs hierarchical regression analysis and fuzzy-set qualitative comparative analysis (fsQCA) to investigate the influence mechanism of AI-enabled job characteristics on work-related flow. Key findings reveal that: AI-enabled job characteristics positively predict work-related flow by increasing perceived challenge stress, yet simultaneously exert a negative influence by exacerbating perceived hindrance stress; techno-efficacy significantly alleviates the relationship between AI-enabled job characteristics and perceived hindrance stress but does not moderate the path via perceived challenge stress; fsQCA identifies four distinct causal configurations of antecedents leading to high work-related flow. This research elucidates the complexities of AI-enabled job characteristics and their dual-faceted impact on work-related flow. By integrating AI into the study of worker psychology and behavior, it extends the contextual scope of job characteristics research. Furthermore, the application of fsQCA provides novel insights into the antecedent conditions and configurational pathways for achieving work-related flow, offering significant theoretical and practical implications. Full article

The spatial distribution of plant species is a crucial indicator of the mechanisms driving competition or coexistence both within and between populations and communities. Analyzing these patterns provides essential insights into fundamental ecological processes and aids in evaluating ecological hypotheses. To study the spatial distribution of dominant tree species and their associations both within and among species, we established a 25-hectare forest plot in Lushan Mountain. We employed the g(r) function alongside three null models—complete spatial randomness (CSR), heterogeneous Poisson (HP), and antecedent condition (AC)—to analyze spatial patterns and assess species interactions at various life stages. Additionally, we examined the relationships between spatial distributions and environmental factors such as soil properties and topography using Berman’s test. Our results showed that all 12 dominant tree species exhibited significant aggregation under the CSR model; however, the scales of aggregation were reduced under the HP model. We also found evidence of aggregation among multiple species across different life stages and tree layers under CSR. Notably, this pattern persisted under the AC model but was limited to specific spatial scales. Furthermore, elevation, topographical convexity, and the total content of soil nitrogen (N) and carbon (C) were identified as statistically significant predictors of species distributions. Overall, these findings highlight that both biological and environmental factors play a vital role in shaping plant spatial patterns across different scales. Full article

The extent to which entrepreneurship education and exposure to role models influence the antecedents of entrepreneurial intention, entrepreneurial intention, and subsequent entrepreneurial behaviour has yielded mixed results in prior research. Furthermore, limited attention has been given to the role of risk-taking propensity in shaping attitude towards behaviour and perceived behavioural control within the Theory of Planned Behaviour (TPB) framework. To address these gaps, this study investigates the influence of entrepreneurship education and role models on the antecedents of entrepreneurial intention, entrepreneurial intention, and entrepreneurial behaviour, drawing on the TPB. In addition, the study examines the effect of risk-taking propensity on both attitude towards behaviour and perceived behavioural control, the relationships between the TPB antecedents and entrepreneurial intention, as well as the direct effects of perceived behavioural control and entrepreneurial intention on entrepreneurial behaviour. Data were collected from 496 final-year diploma students enrolled at a University of Technology and a TVET College in Gauteng, South Africa, using a structured, self-administered online questionnaire. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to analyse the data and test the hypothesised relationships. The findings revealed that entrepreneurship education significantly influences all the antecedents of entrepreneurial intention but does not have a direct influence on entrepreneurial intention or behaviour. Role models had a significant positive effect on perceived behavioural control, subjective norms, and entrepreneurial behaviour, but no effect on attitude towards behaviour or entrepreneurial intention. Risk-taking propensity had a positive effect on both attitude towards behaviour and perceived behavioural control. Furthermore, attitude towards behaviour and perceived behavioural control significantly predicted entrepreneurial intention, while subjective norms did not. Both entrepreneurial intention and perceived behavioural control exerted a significant direct effect on entrepreneurial behaviour. This study highlights the critical role of entrepreneurship education, exposure to entrepreneurial role models, and risk-taking propensity as drivers of entrepreneurial intention and behaviour. Full article