Entrepreneurial resilience in regions is essential for enabling the entrepreneurial ecosystem to overcome natural disasters, catastrophes, wars, and various crisis situations it may face. However, this phenomenon has been underexplored in the literature despite its critical importance for business development, and consequently, for social progress. Therefore, the objective of this article is to conduct a systematic literature review to identify the antecedents of regional entrepreneurial resilience in situations of adversity. To achieve this goal, a qualitative, descriptive research approach was employed. Specifically, a systematic literature review was carried out following the PRISMA method, which included a total of 231 scientific articles retrieved from high impact journals. Of these, only 12% (27 documents) focused on regional entrepreneurial resilience. Five key antecedents of regional entrepreneurial resilience were identified: action orientation, the region’s historical precedents, opportunity exploitation, collaboration, resources, and preparedness. Additionally, it is suggested that future research should focus on understanding the impact of crises, identifying agile response models to crises, defining roles for each member of the entrepreneurial ecosystem to achieve economic recovery in regions, and analyzing the design of public policies that contribute to overcoming adversity. The study concludes that when a region is resilient, it is more likely to overcome crises and adversity.

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 study has formulated the objective of synthesizing the extent to which technological barriers intervene in the transparency and effectiveness of public management (PM). Methodologically, the study was of a fundamental or basic nature, with a systematic review design, the databases of Scopus (369), SciELO (2), Web of Science (184) were explored, after the review process a set of 22 articles was available. The registration was made in an Excel table where the main data of the articles were included. 32% of the articles selected for the analysis of the evidence are from the period 2020, 27% were from 2022 and 18% from the year 2023; as far as origin is concerned, 14% of the articles come from Peru and 9% from Australia, Brazil, South Korea, Spain and Indonesia. In summary, the study points out that government institutions are making progress in digitizing and improving the citizen experience through electronic services, but they face challenges in areas such as resource management, the low adoption of advanced technologies such as blockchain and artificial intelligence, as well as the lack of transparency in PM. Despite this, it is highlighted that e-government improves citizen satisfaction, and the need to invest in digital innovation, training and overcoming technological barriers to achieve an effective transformation in state administration and promote a more inclusive and advanced society is emphasized.

The economy, unemployment, and job creation of South Africa heavily depend on the growth of the agricultural sector. With a growing population of 60 million, there are approximately 4 million small-scale farmers (SSF) number, and about 36,000 commercial farmers which serve South Africa. The agricultural sector in South Africa faces challenges such as climate change, lack of access to infrastructure and training, high labour costs, limited access to modern technology, and resource constraints. Precision agriculture (PA) using AI can address many of these issues for small-scale farmers by improving access to technology, reducing production costs, enhancing skills and training, improving data management, and providing better irrigation infrastructure and transport access. However, there is a dearth of research on the application of precision agriculture using artificial intelligence (AI) by small scale farmers (SSF) in South Africa and Africa at large. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) and Bibliometric analysis guidelines were used to investigate the adoption of precision agriculture and its socio-economic implications for small-scale farmers in South Africa or the systematic literature review (SLR) compared various challenges and the use of PA and AI for small-scale farmers. The incorporation of AI-driven PA offers a significant increase in productivity and efficiency. Through a detailed systematic review of existing literature from inception to date, this study examines 182 articles synthesized from two major databases (Scopus and Web of Science). The systematic review was conducted using the machine learning tool R Studio. The study analyzed the literature review articled identified, challenges, and potential societal impact of AI-driven precision agriculture.

A significant percentage of any nation’s economy comes from the building industry, and its performance can impact overall economic growth and development. This paper aims to identify the similarities and differences between the construction sector (CS) of developed and developing economies in terms of size, growth, and contribution to the Gross domestic product (GDP) to understand the similarities and variances in the CS dynamics, trends, and challenges, and to inform policy decisions and investments through the literature review. The study also explores the factors that affect the CS’s performance in both types of economies, such as government policies, market conditions, and technological advancements. This paper concludes that the CS in developed economies is more established and technologically advanced, but there is still significant room for growth in developing economies. Moreover, a framework is proposed that could assist developing nations in opting for the construction economy. Further, the review emphasizes the significance of government policies and investments in infrastructure development to stimulate the CS’s growth and support overall economic development. The results of the study will assist in enhancing understanding of the CS’s potential in both developed and developing economies and support decision-making for policymakers, industry practitioners, and academicians.