This systematic literature review examines data saturation in qualitative research within the context of entrepreneurship studies from 2004 to 2024. Data saturation, a critical concept in ensuring the rigor of qualitative research, remains inadequately defined in terms of sample size and assessment criteria across various studies. This review synthesizes 11 empirical studies, focusing on strategies such as stopping criterion, code frequency counts, and comparative methods for determining saturation. It identifies sample sizes ranging from 7 to 39 interviews, with an average saturation occurring between 10 and 12 interviews. Furthermore, the study explores the influence of different sampling methods and homogeneity of study populations on saturation outcomes. Despite the reliability of existing methods, the findings underscore the need for greater transparency and consistency in reporting saturation criteria. The review offers valuable insights for entrepreneurial researchers aiming to design qualitative studies, emphasizing the importance of tailored saturation standards based on research objectives and methodologies. This research contributes to a clearer understanding of data saturation in entrepreneurial studies and highlights the necessity for further empirical investigation into saturation across diverse qualitative methods.

The safeguarding of agricultural land is rooted in national land surveys and remote sensing data, which are enhanced by contemporary information technology. This framework facilitates the monitoring and regulation of unauthorized alterations in cultivated land usage. This paper aims to analyze land policies at the national, provincial, and local levels, investigate the cultivated land protection strategies implemented within the research region, where the policies have gained societal acceptance, and propose recommendations and countermeasures to enhance the development and utilization of land resources. The central issue of this study is to identify the challenges in achieving a balance between human activities and natural ecosystems. To address this issue, the research employs a combination of literature review, semi-structured interviews, text analysis, and content analysis, emphasizing the integration of empirical fieldwork and theoretical frameworks. Key areas of focus include: (a) the current state of the farmland protection system, (b) the legal foundations for local enforcement, (c) the systematic mechanisms for implementing arable land protection, and (d) the coordinated oversight system involving both the Party and government. Notably, the practice of cultivated land protection faces several challenges, primarily stemming from two factors. Firstly, there exists a disconnect between the economic interests of certain illegal land users and the objectives of land management, which hinders effective enforcement. Secondly, environmental repercussions arise from misinterpretations of land policy or non-compliant land development practices aimed at profit, which contradict the goals of ecological sustainability. The study examines two approaches to address the issue: the distribution and effective use of land resources, and the capacity for monitoring and early warning systems. Findings indicate that Dongtai City in Jiangsu Province has rigorously implemented all national land management policies, while also preserving the adaptability of local townships in practical applications, thereby ensuring the consistency of both the quality and quantity of arable land.

The aim of this research is to determine the incidence of socioeconomic variables in migration flows from the main countries of origin that form part of the international South-North migration corridor, such as Mexico, China, India, and the Philippines, during the 1990–2022 period. The independent variables considered are GDP per capita, unemployment, poverty, higher education, and public health, while the dependent variable is migration flows. An econometric panel data model is implemented. The tests conducted indicate that all variables have an integration order of I (1) and exhibit long-term equilibrium. The econometric models used, Dynamic Ordinary Least Squares (DOLS) and Fully Modified Ordinary Least Squares (FMOLS), reveal that unemployment and poverty had the strongest influence on migration flows. In both models, within this international migration corridor, GDP per capita, higher education, and health follow in order of importance.

Madura Island, with more than half of its population, are women encountering socio-economic problems, which eventually create high poverty and unemployment rates. However, the Madurese are also well-known for their resiliency and entrepreneurial characteristics. The effort to solve the issues by empowering the community, women in particular, has been taken seriously primarily by entrepreneurs who were born and raised in the community. Therefore, this research aims to gain insight into the current Madurese entrepreneur’s business pattern and their social concerns in order to propose a strategy to increase productivity as an effort to empower women’s communities. The methodology is qualitative research, which collects data using semi-structured interviews with representatives of the Madurese entrepreneurs in four areas of Madura Island. Their responses are then transcripted and coded for content analysis based on the designed themes. The result shows that they recognise and practise the social entrepreneurship (SE) pattern, although they do not understand the term. Subsequently, the technological application for business operations in general is still limited to the usage of digital technology (DT) for marketing and transaction activities, which helps increase business performance or productivity. Hence, the initiation of technosociopreneurship as a strategy to further develop SE activities with the hope of increasing productivity in empowering women’s communities is proposed. Further research development is advised using quantitative methods for generalisable findings.

This study investigates the awareness of environmentally friendly (green) dentistry practices among dental students and faculty at Ajman University in the United Arab Emirates. The primary objective is to assess their understanding and application of eco-friendly dental practices, including waste management, energy conservation, and sustainable material usage. Using a descriptive cross-sectional design, an online survey was administered to 231 randomly selected participants. The results show that although awareness of green dentistry has increased, its practical implementation remains limited. Specialists displayed the highest levels of knowledge and practice, while general practitioners demonstrated the least. Male participants showed greater experience and expertise compared to females, and the age group of 30–39 exhibited the highest levels of knowledge and practice, although age was not found to significantly affect awareness or usage. The findings highlight the need for enhanced education and encouragement of green dentistry to protect the environment and promote sustainable dental practices.

This paper explores the integration of Large Language Models (LLMs) and Software-Defined Resources (SDR) as innovative tools for enhancing cloud computing education in university curricula. The study emphasizes the importance of practical knowledge in cloud technologies such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), DevOps, and cloud-native environments. It introduces Lean principles to optimize the teaching framework, promoting efficiency and effectiveness in learning. By examining a comprehensive educational reform project, the research demonstrates that incorporating SDR and LLMs can significantly enhance student engagement and learning outcomes, while also providing essential hands-on skills required in today’s dynamic cloud computing landscape. A key innovation of this study is the development and application of the Entropy-Based Diversity Efficiency Analysis (EDEA) framework, a novel method to measure and optimize the diversity and efficiency of educational content. The EDEA analysis yielded surprising results, showing that applying SDR (i.e., using cloud technologies) and LLMs can each improve a course’s Diversity Efficiency Index (DEI) by approximately one-fifth. The integrated approach presented in this paper provides a structured tool for continuous improvement in education and demonstrates the potential for modernizing educational strategies to better align with the evolving needs of the cloud computing industry.