Search Results (1,253)

Part-time employment is an increasingly important feature of the U.S. labor market, yet little is known about how earnings determinants differ between full-time and part-time pharmacists. Few prior studies have compared earnings models across these groups, but most have relied on small or geographically limited samples. Moreover, the dynamic and rapidly evolving nature of the labor market makes this study especially timely, as most prior research on pharmacist earnings is based on older data. This study examined earnings determination separately for full-time and part-time pharmacists, estimating the influence of work input, human capital, demographic characteristics, and job-related features within each group. Data were obtained from the 2019–2022 American Community Survey (ACS), a large, continuous, nationally representative survey conducted annually by the U.S. Census Bureau. The sample included 12,064 pharmacists (4667 men and 7397 women) aged 25–64 years, practicing in the U.S. Ordinary least-squares equations were estimated separately for male and female pharmacists within each employment category, allowing comparison of the direction, magnitude, and statistical significance of covariates across groups. Results revealed notable differences in the earnings effects of several factors between full-time and part-time pharmacists, highlighting the interaction of individual choices and structural market forces in shaping compensation. These findings can inform workforce planning and guide the development of targeted job-related incentives to support retention and satisfaction across employment types. Full article

This study investigates the determinants of service demand in the packaging and printing industry, identifying 19 key factors through expert evaluation. These factors were analyzed using the Importance–Satisfaction (I–S) Model to pinpoint areas requiring enhancement, with four elements classified within the improvement zone. Considering resource constraints, improvement priorities were established through a modified Risk Priority Number (RPN) framework derived from Failure Modes and Effects Analysis (FMEA), expressed as RPN = I × F × E. The highest-priority areas for improvement included product pricing, flexibility in meeting customer requirements, suppliers’ emergency response capabilities, and proactive communication regarding raw material price fluctuations. The findings indicate that consumers balance price against sustainability value, highlighting the necessity of setting prices that align with perceived value to sustain trust and meet expectations. Strengthening firms’ emergency response mechanisms and developing an online standard operating procedure (SOP) notification system for raw material price changes can enhance communication efficiency, increase transparency in pricing, and ultimately improve organizational competitiveness. Full article

Understanding entrepreneurial intention among female students has become increasingly important for addressing gender disparities in business creation and fostering economic development. Pursuing to promote inclusive entrepreneurship and reduce gender gaps in business creation aligns with Sustainable Development Goals, particularly SDG 5 (gender equality). This study examines how demographic and social variables influence entrepreneurial intentions, with perceived gender discrimination as a potential mediating factor. Data were collected through an online survey employing a structured questionnaire and analyzed using logistic regression models incorporating mediation analysis. The sample consisted of 360 female students from a university in the South–East region of Romania. Among the six socio-demographic variables examined, marital status and income satisfaction emerge as significant predictors. The results indicated that married students expressed higher entrepreneurial intentions, while the participants with higher income satisfaction reported lower entrepreneurial intentions. Perceived gender discrimination was not a significant mediator in the tested model, and all calculated indirect effects were statistically non-significant. The findings of the study offer valuable insights for the design and implementation of local entrepreneurship policies as well as for university strategies and curricula adjustments to better support young women in their entrepreneurship endeavors. Full article

This study introduces the Humanizing Sustainable Corridors Framework (HSCF), developed to guide the transformation of Car-Oriented corridors into Human-centered, sustainable spaces. Rooted in a human-centered approach, the framework emphasizes enhancing social interaction, addressing environmental needs, and supporting local economies through urban design. The framework was applied to the Al-Hada Ring Road in Taif, Saudi Arabia, as a case study. A mixed-methods approach was utilized, incorporating expert field observations, interviews with 15 stakeholders, and a web-based survey that yielded 455 valid responses. The findings revealed that 78% of respondents prioritized natural landscapes, 72% highlighted the importance of walkability, and 69% emphasized the need for shaded areas and culturally rooted design elements that enhance comfort and safety. These results demonstrate that planning strategies reflecting local climate conditions, user behavior, and cultural identity can increase corridor sustainability and resilience by over 65% in terms of perceived user satisfaction and safety. The HSCF offers a structured, adaptable model for planners and decision-makers seeking to align spatial design with community needs and national development goals. Full article

In the current economic context, organizations providing automotive repair services face significant challenges in ensuring service quality, operational efficiency, and long-term sustainability. This paper examines the importance of implementing process monitoring systems through the integration of European quality frameworks and Japanese operational principles such as Kaizen, Lean Manufacturing, and Poka-Yoke, to improve the quality of services and increase performance within automotive repair organizations. The research is grounded in Sustainable Development Goals (SDG 9—Industry, Innovation and Infrastructure, and SDG 12—Responsible Consumption and Production), demonstrating how structured quality practices contribute to reducing waste, optimizing processes, and delivering responsible services. The main objectives of the study are to identify the elements that influence the performance of service-specific processes, to improve the quality management practices related to these processes, to eliminate non-conformities, and to enhance profitability and competitive differentiation through service quality assurance. A mixed-methods research design was applied, including direct participatory observation, performance monitoring, and correlational statistical analysis over a six-month period in two Romanian automotive service centers. Key performance indicators (KPIs) such as technician efficiency, rework rate, and order throughput time were collected and analyzed before and after the implementation of selected tools. Findings demonstrate measurable improvements: rework rates decreased from 7.8% to 2.6%, technician efficiency improved from 89% to 105%, and average service completion time was reduced by 1.6 days. Correlation analysis confirmed strong relationships between visual management adoption and rework reduction (r = −0.75), as well as between Lean implementation and technician efficiency (r = +0.89). The study’s novelty lies in its integration of cross-cultural quality management practices into a replicable and sustainable operational model for post-sale service environments. The results validate that implementing monitoring systems, combined with Kaizen, Lean, and Poka-Yoke, supported by visual management and active employee engagement, can lead to superior service quality management, increased customer satisfaction, and long-term organizational success in the automotive repair industry. Full article

This study examines the intention of Greek tourists who visit national touristic destinations to adopt Artificial Intelligence (AI) chatbots in the tourism sector. Using the UTAUT2 model as a framework, data were collected through a closed-ended questionnaire and analyzed with correlation and regression methods to identify the main drivers and barriers to this adoption. Results show that specific factors such as performance expectancy, hedonic motivation, and perceived innovativeness significantly and positively influence chatbot usage, emphasizing the role of usefulness, enjoyment, and innovation in shaping user acceptance. Conversely, factors such as inconvenience, habit, and difficulty of use negatively affect adoption, indicating the importance of overcoming usability challenges and resistance to change. These findings highlight the need for the development of accessible and engaging chatbot systems and underscore the value of continuous technological improvements. The study concludes that adopting AI-driven solutions can help tourism providers personalize services, improve operational efficiency, and enhance customer satisfaction, fostering sustainable competitiveness in the sector. Full article

Parking shortages and high costs in Chinese central business districts (CBDs) remain major urban challenges. Emerging automated vehicles (AVs) are expected to diversify parking options and mitigate these problems. However, AV users’ parking preferences and their influencing factors within existing urban zoning frameworks remain unclear. This study examines Nanjing as a representative case, proposing six distinct AV parking modes. Using survey data from 4644 responses collected from 1634 potential users, we employed nested logit models and random forest algorithms to analyze parking choice behavior. Results indicate that diversified AV parking modes would significantly reduce CBD parking demand. Users with medium- to long-term needs prefer home-parking, while short-term users favor CBD proximity. Key influencing factors include parking service satisfaction, duration, congestion time, AV punctuality, and individual characteristics, with satisfaction attributes showing the greatest impact across all modes. Comparative analysis reveals that random forest algorithms provide superior predictive accuracy for parking mode importance, while nested logit models better explain causal relationships between choices and influencing factors. This study establishes a dual analytical framework combining interpretability and predictive accuracy for urban AV parking research, providing valuable insights for transportation management and future metropolitan studies. Full article

Local food (LF) consumption has achieved increasing attention over the last few decades, given its potential to enhance social, economic, and environmental sustainability. Despite its benefits, understanding consumer behaviour towards LF remains underexplored. This study investigates intrinsic and extrinsic motivations for LF consumption among young Italian consumers, applying Self-Determination Theory (SDT). Using structural equation modelling (SEM) on a sample of 931 respondents, this study reveals the significant roles of perceived benefits, knowledge, satisfaction, and food sustainability concern (FSC) in shaping people’s intention to consume LF. Moreover, the findings underscore the importance of food sustainability concerns, as an extrinsic motivation improving the effect of the intrinsic ones, thus fostering a persistent intention to consume local food. Full article

Radio spectrum planning has become increasingly important, since the radio spectrum is a scarce resource. Moreover, the utilization of millimeter wave (mmWave) frequencies with fifth-generation (5G) standards has made radio planning more compelling. Considering their different strengths and weaknesses, it is essential to know when mmWave frequencies should be selected in radio planning. In this paper, an approach with edge learning is developed to provide smart spectrum recommendations on which frequency bands should be used for a region. Using the proposed approach, radio spectrum planning can be carried out more efficiently, especially for the frequency ranges of mmWave communications. The proposed approach is designed with a distributed structure, based on awareness of the environment and ambient intelligence. This approach can be performed for each transmission point considering the environment information of the related coverage area. As a result, radio spectrum planning can be conducted for an entire region with the proposed system. The results show that this study both enhances overall user satisfaction and provides reasonable recommendations to operators in the transition to mmWave usage. Thus, the developed approach can be utilized for 5G and beyond communications. Specifically, this methodology is based on applying supervised ML algorithms to a synthetically generated dataset, and the best model achieves around 80% classification accuracy, demonstrating the feasibility of the approach. These quantitative results confirm its practicality and provide a concrete baseline for future studies. Full article

Given the importance of relationship satisfaction and the detrimental effects of its decline in romantic couples, it is crucial to understand how relationship satisfaction develops over time in long-term stable relationships and to identify predictors that explain such long-term changes. Building upon previously identified subgroups with distinct trajectories of relationship satisfaction, our objective was to examine whether two types of relationship skills—dyadic coping and communication—predict subgroup trajectories. We followed 300 mixed-gender couples over 10 years in annual assessments and applied Dyadic Latent Class Growth models with predictors. Our results suggest that subgroups of relationship satisfaction trajectories can be differentiated by both baseline levels and changes in relationship skills. Couples with high and relatively stable satisfaction were distinguished from those with declining satisfaction primarily by baseline negative communication (women’s report) and a deterioration in dyadic coping. Couples with the lowest initial satisfaction exhibited the least beneficial relationship skills but increased their satisfaction over time, likely due to observed improvements in their skills. These findings have important public health implications, as modifiable relationship skills can be targeted in prevention, counseling, or therapy to help couples develop and sustain improvements in their relationship skills to protect their relational well-being in the long term. Full article

Cultural heritage sites play a crucial role in safeguarding identity, fostering cultural exchange, and generating sustainable tourism. Within this context, the Palace Museum in Beijing, which attracts 19 million annual visitors, offers a compelling case for examining the dynamics that shape revisit intention. This study explores the relationships among perceived authenticity, place attachment, destination satisfaction, visitor engagement, and revisit intention within the context of heritage tourism. Using Partial Least Squares–Structural Equation Modeling (PLS-SEM), data were collected from local visitors to the Palace Museum to analyze both the direct and mediating effects of these constructs. Findings indicate that perceived authenticity significantly enhances both destination satisfaction and visitor engagement, while place attachment makes a strong contribution to visitor engagement. Moreover, visitor engagement emerged as a more influential mediator than destination satisfaction in linking perceived authenticity to revisit intention, showing the importance of immersive and meaningful participation in shaping tourists’ behavioral intentions. These results suggest that while satisfaction remains a relevant concept, strategies that emphasize authenticity-driven experiences and fostering of deeper emotional and participatory bonds are more effective in sustaining revisits. This study advances the understanding of heritage tourism and provides practical insights for managing iconic heritage sites such as the Palace Museum. Full article

Accurate prediction of food delivery time is crucial for enhancing operational efficiency and customer satisfaction in real-world logistics and intelligent dispatch systems. To address this challenge, this study proposes a novel symmetric optimization prediction framework, termed SymOpt-CNSVR. The framework is designed to leverage the strengths of both deep learning and statistical learning models in a complementary architecture. It employs a Convolutional Neural Network (CNN) to extract and assess the importance of multi-feature data. An Enhanced Superb Fairy-Wren Optimization Algorithm (ESFOA) is utilized to optimize the diverse hyperparameters of the CNN, forming an optimal adaptive feature extraction structure. The significant features identified by the CNN are then fed into a Support Vector Regression (SVR) model, whose hyperparameters are optimized using Bayesian optimization, for final prediction. This combination reduces the overall parameter search time and incorporates probabilistic reasoning. Extensive experimental evaluations demonstrate the superior performance of the proposed SymOpt-CNSVR model. It achieves outstanding results with an R² of 0.9269, MAE of 3.0582, RMSE of 4.1947, and MSLE of 0.1114, outperforming a range of benchmark and state-of-the-art models. Specifically, the MAE was reduced from 4.713 (KNN) and 5.2676 (BiLSTM) to 3.0582, and the RMSE decreased from 6.9073 (KNN) and 6.9194 (BiLSTM) to 4.1947. The results confirm the framework’s powerful capability and robustness in handling high-dimensional delivery time prediction tasks. Full article

Beliefs about personhood held by healthcare professionals and care partners influence care outcomes, satisfaction, and the well-being of persons living with dementia (PLWD). This study examined differences in personhood beliefs based on demographic and contextual factors, including the Social Vulnerability Index (SVI), using registration data from the Bravo Zulu care partner training program (n = 540). Guided by the Ring Theory of Personhood, eight factors were analyzed: age, sex, race, socioeconomic status, professional discipline, healthcare experience, prior care partner training, and SVI. One-way ANOVA and independent t-tests were used to examine group-level differences, and multiple linear regression was conducted to assess the extent to which these factors predicted personhood beliefs. Race, age (borderline significance) professional discipline, and prior training as a care partner were significant predictors of personhood beliefs. Subscale analyses using ANOVA and t-test showed that beliefs about psychosocial engagement varied by SVI and healthcare experience with small effect size; however, these factors did not significantly predict of overall personhood beliefs in the regression model. Findings underscore the importance of recognizing how background characteristics shape personhood beliefs about PLWD. Promoting self-reflection and expanding culturally responsive training may support person- and relationship-centered care and improve satisfaction in multicultural care settings. Full article

Background: Degenerative lumbar spine disease is a prevalent cause of chronic low back pain that significantly impairs daily function and quality of life. While conservative management is the first line of treatment, many patients ultimately require instrumented lumbar spine surgery. However, postoperative outcomes vary considerably, with emerging evidence suggesting that preoperative psychological factors such as anxiety, depression, and pain catastrophizing may influence recovery. The SAP-LD (Scale for Anxiety and Pain in Lumbar Degeneration) study was designed to assess the prognostic role of these psychological and physical parameters in surgical outcomes. Methods: This prospective observational study enrolled 70 adult patients with degenerative lumbar spine pathology scheduled for instrumented surgical treatment at the University of Brescia and ASST Spedali Civili di Brescia between March and December 2024. Preoperative assessments included demographic, clinical, and radiologic data along with validated scales: the Oswestry Disability Index (ODI), 36-Item Short Form Health Survey (SF-36), Visual Analogue Scale (VAS), Pain Catastrophizing Scale (PCS), and Hospital Anxiety and Depression Scale (HADS). Follow-up evaluations were performed at 45 days and at 6 months, and statistical analyses were conducted using correlation tests, ANOVA, and regression modeling. Results: The demographic analysis of the 70 enrolled patients shows a balanced gender distribution (38 females, 34 males) with a mean age of 61 years (range 23–81). The educational level distribution indicates that the majority of patients (44.29%) have a secondary education level, while 35.71% have a tertiary education level. Regarding employment status, 50% of the patients are retired or not working. Patients with clinically significant anxiety and/or depression showed higher levels of perceived pain, pain catastrophizing, and disability at baseline. These patients reported significantly worse scores on the Visual Analogue Scale (VAS), Pain Catastrophizing Scale (PCS), and Oswestry Disability Index (ODI). The Oswestry Disability Index (ODI) demonstrates a clinically significant improvement (reduction) in disability between the preoperative period (t0) and the 45-day follow-up (t2), with the median decreasing from 39.00 to 13.00. However, there is a partial regression at the 6-month follow-up (t3), with the median increasing to 27.00. For the SF-36 Health Survey, the General Health subscale shows an improvement between t0 and t2 (median increasing from 55.00 to 60.00), followed by a slight decrease at t3 (median 55.00). Similar patterns are observed in most other subscales, with initial improvement followed by partial regression. The Pain Catastrophizing Scale (PCS) shows a substantial reduction in catastrophizing between t0 and t2 (median decreasing from 16.00 to 3.00), followed by an increase at t3 (median 11.00), though still below baseline levels. Pain intensity as measured by the Visual Analogue Scale (VAS) shows a significant reduction at t2 (median decreasing from 5.00 to 3.00), but increases again at t3 (median 6.00), even exceeding the preoperative level. For the Hospital Anxiety and Depression Scale (HADS), no significant differences were observed across time points, with values indicating mild symptoms throughout the study period. Correlation analyses confirmed that higher preoperative anxiety and depression scores were predictive of poorer postoperative outcomes. Specifically, higher HADS scores at baseline are associated with higher ODI scores (increased disability) at all time points (p = 0.002), higher VAS scores (increased pain) at all time points (p = 0.015), and lower scores on SF-36 subscales, particularly Emotional Well-being (p = 0.00023) and Social Functioning (p = 0.002). Higher PCS scores at baseline are associated with higher ODI scores at all time points (p = 0.001), higher VAS scores at all time points (p = 0.008), and lower scores on SF-36 subscales, particularly Pain (p = 0.00023) and Physical Functioning (p = 0.04254). The mixed linear models analysis confirms these findings, showing that the ODI score decreases significantly between t0 and t2 (p = 0.00023) and increases between t2 and t3, though this increase is not statistically significant (p = 0.079). For VAS scores, there is a significant decrease between t0 and t2 (p = 0.00023) and a significant increase between t2 and t3 (p = 0.04254). Patients with elevated preoperative HADS scores tended to have slower recovery trajectories and reported lower satisfaction levels. These findings reinforce the prognostic value of psychological assessments in spine surgery and suggest that targeted psychological interventions could improve patient outcomes. Conclusions: By identifying psychological predictors of postoperative recovery, this study underscores the importance of integrating preoperative psychological screening into routine clinical practice. The results suggest that a multidisciplinary approach, including both surgical and psychological care, could enhance long-term functional outcomes and quality of life for patients undergoing instrumented lumbar spine surgery. Full article

Integrating Artificial Intelligence (AI) into education poses new challenges and opportunities, particularly in the training of university professors, where Teaching Digital Competence (TDC) emerges as a key factor to leverage its potential. The aim of this study was to evaluate a structural model designed to measure TDC in relation to the educational use of AI. A quantitative methodology was applied using a validated questionnaire distributed through Google Forms between March and May 2024. The sample consisted of 368 university professors. The model examined relationships among key dimensions, including cognition, capacity, vision, ethics, perceived threats, ai-powered innovation, and job satisfaction. The results indicate that cognition is the strongest predictor of capacity, which in turn significantly influences vision and ethics. AI-powered innovation presented limited explained variance, while perceived threats from AI negatively affected capacity. Additionally, job satisfaction was mainly influenced by external factors beyond the model. The overall model fit confirmed its reliability in explaining the proposed relationships. This study highlights the critical role of cognitive training in AI for teachers and the importance of designing targeted professional development programs to enhance TDC. Although a generally positive attitude towards AI was identified, perceptions of threats remained low. Full article