Human resources (HR) analytics is garnering increasing interest each year and is set to play a pivotal role in the development of human resources. In the present era, numerous companies are harnessing the power of analytics to gain a competitive advantage by comprehending all the vital aspects of their workforce by enhancing employee retention through leveraging HR analytics to inform strategic HR choices. Many companies are now incorporating analytical tools into their HR function as a fact-based approach to develop relevant strategies and make informed decisions in managing their workforce more effectively. However, HR faces several challenges in implementing data analytics. Talent management commonly utilizes data analytics to enhance employee engagement, including retention rates, recruitment, job satisfaction, and happiness. This paper discusses the adoption of HR data analytics to enhance employee retention in the workplace. This study delves into the significance of HR data analytics in the realm of employee retention, aiming to assess the efficacy of data-driven decisions. A thorough examination of scholarly publications was undertaken, encompassing both indexed and non-indexed papers sourced from reputable electronic databases to gain insights into the present understanding of HR analytics and its influence on employee retention. The discussion uncovers that HR analytics has a noteworthy impact on improving employee retention in the workplace.

Metal organic framework is a class of hybrid network of supramolecular solid materials comprised of a large number of inorganic and organic linkers all bounded to metal ions in a well-organized fashion. This type of compounds possess a greater surface area with an advantage of changing pore sizes, diversified and beautiful structure which withdrew an intense interest in this field. In the present review articles, the structural aspects, classification, methods of synthesis, various factors affecting the synthesis and stability, properties and applications have been discussed. Recent advances in the field and new directions to explore the future scope and applications of MOFs have been incorporated in this article to provide current status of the field.

The activities and characteristics of heritage, cultural, and creative tourism are notably distinct despite the fact that they are frequently confused and misunderstood. Moreover, these types of tourism have been significantly affected by the COVID-19 pandemic. This review article aims to explore the characteristics of three types of tourism, both pre- and post-pandemic, and seeks to propose sustainable solutions with new opportunities for the tourism industry. The article adopts a PRISMA flow diagram and VOSviewer to perform a systematic literature review, ultimately selecting 179 articles from the Scopus, ScienceDirect, and Google Scholar databases and grouping them into five clusters: 1) heritage, cultural, and creative tourism; 2) co-creation; 3) creative city; 4) sustainability; and 5) technology and innovation. Consequently, this review article proposes a final framework presenting five related clusters suggesting sustainable solutions for creative tourism. It may aid the tourism industries in their transition to creative tourism, which is more sustainable and broadly focused while ensuring safety and enhancing income for local communities in the post-pandemic period.

The destructive geohazard of landslides produces significant economic and environmental damages and social effects. State-of-the-art advances in landslide detection and monitoring are made possible through the integration of increased Earth Observation (EO) technologies and Deep Learning (DL) methods with traditional mapping methods. This assessment examines the EO and DL union for landslide detection by summarizing knowledge from more than 500 scholarly works. The research included examinations of studies that combined satellite remote sensing information, including Synthetic Aperture Radar (SAR) and multispectral imaging, with up-to-date Deep Learning models, particularly Convolutional Neural Networks (CNNs) and their U-Net versions. The research categorizes the examined studies into groups based on their methodological development, spatial extent, and validation techniques. Real-time EO data monitoring capabilities become more extensive through their use, but DL models perform automated feature recognition, which enhances accuracy in detection tasks. The research faces three critical problems: the deficiency of training data quantity for building stable models, the need to improve understanding of AI's predictions, and its capacity to function across diverse geographical landscapes. We introduce a combined approach that uses multi-source EO data alongside DL models incorporating physical laws to improve the evaluation and transferability between different platforms. Incorporating explainable AI (XAI) technology and active learning methods reduces the uninterpretable aspects of deep learning models, thereby improving the trustworthiness of automated landslide maps. The review highlights the need for a common agreement on datasets, benchmark standards, and interdisciplinary team efforts to advance the research topic. Research efforts in the future must combine semi-supervised learning approaches with synthetic data creation and real-time hazardous event predictions to optimise EO-DL framework deployments regarding landslide danger management. This study integrates EO and AI analysis methods to develop future landslide surveillance systems that aid in reducing disasters amid the current acceleration of climate change.

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.