This study investigates the potential of developing a maritime tourism project within the blue economy of Pakistan and explores the factors influencing blue growth and maritime tourism. A quantitative research approach has been adopted. The research gathered primary data from diverse experts and stakeholders within the maritime sector and related industries. The study’s target population comprised on various entities involved in these sectors. A sample of around 250 individuals was selected using a convenient sampling technique. The collected data underwent analysis using the Statistical Package for the Social Sciences (SPSS) and the Partial Least Square (PLS) method. This approach was chosen to explore and understand the intricate relationships between variables in the context of the maritime industry. Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA) techniques were then employed to scrutinize the data further, allowing for a comprehensive examination of the interconnections among the variables identified in the study. This robust methodological approach enhances the study’s credibility and provides valuable insights into the dynamics of the maritime sector and its associated industries. The findings indicate that a balanced approach, valuing business sustainability, top management support, and enabling innovation structures positively impact blue growth. Additionally, uncertainty avoidance and promoting short-term goals have an appositive impact on the blue economy. Moreover, two potential barriers, Functional strategy, and weak competency, do not significantly affect the blue economy. This study lays the foundation for further exploration and implementation of strategies that promote sustainable growth and development in Pakistan’s blue economy. By integrating the insights gained from this study into policy and decision-making processes, stakeholders can work together to create a vibrant and sustainable maritime tourism sector that benefits both local communities and the environment.

In recent years, how farmers leverage social capital to improve their well-being has become a crucial question in post-poverty alleviation China. This study assessed the impact of ‘linking social capital’ on farmers’ well-being, as mediated by self-efficacy. The study was conducted using data collected from 443 randomly selected farmers from two villages in Guizhou Province, China. The Partial Least Squares Structural Equation Model (PLS-SEM) was employed to analyze the proposed relationships in the study. The results indicate that linking social capital, when mediated by self-efficacy, positively impacted farmers’ well-being. This suggests that policymakers and implementers exercising hierarchical power in social improvement programs in disadvantaged provinces, such as Guizhou, should take full advantage of linking social capital to effectively improve farmers’ well-being. In doing so, the study concludes, they should consider the positive role farmers’ self-efficacy can play in the process.

Introduction: In Central Europe, in Hungary, the state guarantees access to health care and basic health services partly through the Semmelweis Plan adopted in 2011. The primary objectives of the Semmelweis Plan include the optimisation and transformation of the health care system, starting with the integration of hospitals and the state control of previously municipally owned hospitals. The transformation of the health care system can have an impact on health services and thus on meeting the needs of the population. In addition to reducing health inequalities and costs, the relevant benefits include improving patients’ chances of recovery and increasing patient safety. The speciality under study is decubitus care. Our hypothesis is that integration will improve the chances of recovery for decubitus patients through access to smart dressings to promote patient safety. Objective: to investigate and demonstrate the effectiveness of integration in improving the chances of recovery for decubitus ulcer patients. Material and methods: The research compared two time periods in the municipality of Kalocsa, Bács-Kiskun County, Southern Hungary. We collected the number of decubitus patients arriving and leaving the hospital from the nursing records and compared the pre-integration period when decubitus patients were provided with conventional dressings (01.01.2006–2012.12.31) and the post-integration period, which entailed the introduction of smart dressings in decubitus care (01.01.2013–2012.12.31). The target population of the study was men and women aged 0–99 years who had developed some degree of decubitus. The sample size of the study was 4456. Independent samples t-test, Chow test and linear trend statistics were used to evaluate the results. Based on the empirical evidence, a SWOT analysis was conducted to further examine the effectiveness of integration. Results: The independent samples t-test model used was significant (for Phase I: t (166) = −16.872, p < 0.001; for Phase II: t (166) = −19.928, p < 0.001; for Phase III: t (166) = −19.928, p < 0.001; for Phase III: t (166) = −16.872, p < 0.001). For stage III: t (166) = −10.078, p < 0.001; for stage IV: t (166) = −10.078, p < 0.001; for stage III: t (166) = −10.078, p < 0.001). for stage III: t (166) = −14.066, p < 0.001). For the Chow test, the p-values were highly significant, indicating a structural break. Although the explanatory power of the regression models was variable (R-squared values ranged from 0.007 to 0.617), they generally supported the change in patient dynamics after integration. Both statistical analyses and SWOT analysis supported our hypothesis and showed that integration through access to smart dressings improves patients’ chances of recovery. Conclusions: Although only one segment of the evidence on the effectiveness of hospital integration was examined in this study, integration in the study area had a positive impact on the effective care of patients with decubitus ulcers, reduced inequalities in care and supported patient safety. In the context of the results obtained, these trends may reflect different systemic changes in patient management strategies in addition to efficient allocation of resources and quality of care.

Background: In healthcare, research is essential for improving disease diagnosis and treatment, patient outcomes, and resource management, while fostering evidence-based practice. However, conducting research in this sector can be challenging, and healthcare workers may face various obstacles while engaging in research activities. Therefore, understanding healthcare workers’ attitudes toward research participation is essential for overcoming barriers and increasing research engagement. In this study, these aspects are examined through the analysis of survey data from a tertiary healthcare institution in Saudi Arabia. Method: Data obtained via a survey conducted between April and November 2022 among the healthcare workers and employees at a tertiary care hospital in Saudi Arabia were analyzed using descriptive and bivariate statistics. Results: The study sample comprised 713 respondents, 61.71% of whom were female, 58.06% were 26–41 years old, and 72.93% had not undertaken any research as employees or affiliates. A significant association was noted between age group and time constraints (p = 0.004) and lack of opportunity for research (p = 0.00), which were among the identified barriers to research participation. A significant association was also found between gender and barriers to pursuing research (p = 0.012). When the 193 (27.07%) participants who conducted research were asked about the challenges they encountered during this process, gender was significantly associated with difficulties in allocating time for conducting research (p = 0.042) and challenges in accessing journals and references (p = 0.016). Conclusion: The study findings highlight the importance of addressing the barriers and challenges in promoting positive attitudes toward research participation among healthcare workers considering their gender and age. In this manner, healthcare institutions can adopt an environment conducive for professional research engagement.

Tourism is one of the important sectors that support Indonesia’s economic growth. The tourism sector itself plays a strategic role in increasing the country’s foreign exchange. However, during the Covid-19 pandemic, tourism became one of the most affected sectors. Electronic visa on arrival (e-VOA) is a form of digital transformation in immigration services offered by the Indonesian government to increase the number of tourist arrivals during the recovery of the national economy, especially in the tourism sector, after the Covid-19 pandemic. This study provides an in-depth insight into how e-VOA functions as a digital transformation tool in the immigration and tourism sectors. By exploring the impact of e-VOA implementation, this article contributes to the understanding of how digitalisation can improve the efficiency of administrative processes and support the recovery of the tourism sector in post-pandemic Bali. This study uses qualitative approaches and methods with descriptive analysis techniques to create an objective description of a situation through numbers or statistical data. The results of this study show that e-VOA services effectively contribute to an increase in the number of foreign tourists in Bali. It also has a positive impact on the economic growth of tourism-related businesses in Bali.

China’s graduate quality management system is designed to ensure that students possess the necessary skills, knowledge, and competencies for future success. This system is rooted in China’s ambitious educational reforms aimed at cultivating a highly skilled workforce to drive economic growth and innovation. Effective graduate quality management significantly impacts employment levels, training models, and national policy formulation. This study investigates the quality management approaches of 56 vocational institutions in Yunnan Province using a 5-level questionnaire and a quantitative research methodology. A sample of 556 individuals was selected through stratified random sampling. Exploratory factor analysis identified five primary components of the quality management model: College graduate quality (mean = 4.56, SD = 0.49), teaching quality (mean = 4.39, SD = 0.42), hardware environment (mean = 4.38, SD = 0.44), social support (mean = 4.37, SD = 0.42), and job satisfaction (mean = 4.38, SD = 0.42). College graduate quality and teaching quality were the most influential factors, while hardware environment, social support, and job satisfaction had lesser impacts.