This study examines the impact of artificial intelligence (AI) on loss in decision-making, laziness, and privacy concerns among university students in Pakistan and China. Like other sectors, education also adopts AI technologies to address modern-day challenges. AI investment will grow to USD 253.82 million from 2021 to 2025. However, worryingly, researchers and institutions across the globe are praising the positive role of AI but ignoring its concerns. This study is based on qualitative methodology using PLS-Smart for the data analysis. Primary data was collected from 285 students from different universities in Pakistan and China. The purposive Sampling technique was used to draw the sample from the population. The data analysis findings show that AI significantly impacts the loss of human decision-making and makes humans lazy. It also impacts security and privacy. The findings show that 68.9% of laziness in humans, 68.6% in personal privacy and security issues, and 27.7% in the loss of decision-making are due to the impact of artificial intelligence in Pakistani and Chinese society. From this, it was observed that human laziness is the most affected area due to AI. However, this study argues that significant preventive measures are necessary before implementing AI technology in education. Accepting AI without addressing the major human concerns would be like summoning the devils. Concentrating on justified designing and deploying and using AI for education is recommended to address the issue.

Despite the issues that the hospitality industry encounters in retaining talented employees, little attention has been paid to the development of retention strategies, resulting in poor organizational performance and sustainable growth. The current study, therefore, aims to review and discuss the employee retention strategies in the hospitality industry in order to keep talented employees for a longer period of time. The study is based on past literature and peer-reviewed articles published between 2010 and 2020. The databases Web Knowledge, Emerald, Google Scholar, and Science Direct were used to find the relevant papers using the key words such as “Hospitality Industry”, “employee retention”, “employee turnover”, and “employees”. The findings of this study suggest that employee retention is contingent on employee satisfaction, which is comprised of four factors: sustainable positive work environment; sustainable growth opportunities; sustainable & effective communication; and sustainable & effective recruitment and selection practices. The paper contributes to a comprehensive review of the literature on employee retention strategies in the hospitality context. The study proposes a model for the hospitality industry to revamp its recruitment and selection practices in order to retain its employees. Furthermore, the study provides a focused directions that will aid in the establishment of employee retention strategies and practices. It was concluded that satisfied employees are less likely to leave their current job, while unsatisfied employees are expected to leave their current job for a better career opportunity. Managerial implications were also discussed.

Technical and methodological enhancement of hazards and disaster research is identified as a critical question in disaster management. Artificial intelligence (AI) applications, such as tracking and mapping, geospatial analysis, remote sensing techniques, robotics, drone technology, machine learning, telecom and network services, accident and hot spot analysis, smart city urban planning, transportation planning, and environmental impact analysis, are the technological components of societal change, having significant implications for research on the societal response to hazards and disasters. Social science researchers have used various technologies and methods to examine hazards and disasters through disciplinary, multidisciplinary, and interdisciplinary lenses. They have employed both quantitative and qualitative data collection and data analysis strategies. This study provides an overview of the current applications of AI in disaster management during its four phases and how AI is vital to all disaster management phases, leading to a faster, more concise, equipped response. Integrating a geographic information system (GIS) and remote sensing (RS) into disaster management enables higher planning, analysis, situational awareness, and recovery operations. GIS and RS are commonly recognized as key support tools for disaster management. Visualization capabilities, satellite images, and artificial intelligence analysis can assist governments in making quick decisions after natural disasters.

This article aims to provide a critical review of the scientific research on technostress. As such, global references in this field are identified and highlighted in order to manage pre-existing knowledge and establish future ‘bridges’ among researchers, and to enhance the presently dispersed understanding of this subject. A scientometric meta-analysis of publications on technostress was conducted to achieve this objective. Mainstream journals from the Web of Science (WoS) were used to identify current topics, relevant journals, prolific authors, institutions, and countries, ‘schools of thought’ and the thematic areas around which current technostress debate revolves. In this article a significant contribution comes from the use of the scientific activity itself, together with scientometric meta-analysis techniques and the application of this scientific activity, its impact and relational character, to discover relevant countries, research organizations and authors which can constitute a global reference to demarcate this knowledge frontier, and who lead the ‘critical mass’ of global technostress researchers. This study also distinguishes between the relevant themes studied, with co-keywords plus bibliographic coupling citation, and examines the kind of stress the most prolific authors have considered and, therefore, to discover those topics which should be studied further to deepen this research field, in search of a post-disciplinary knowledge that allows unity of focus in technology and psychology.

The presence of milk in meals and products consumed daily is common and at the same time the adoption of a milk-free diet increases due to milk allergy, lactose intolerance, vegan diets, and others. Therefore, there is an increasing demand for plant-based beverages, which present variable and, sometimes, unknown nutritional characteristics. This study sought to compare the nutritional aspects of plant-based beverages used as substitutes for cow’s milk described in scientific studies. Therefore, we used a review of the scientific literature on PubMed, Google Scholar, Scopus, Web of Science, Google Patents, Embase, and ScienceDirect databases. The inclusion criteria were scientific studies referring to plant-based beverage used as an alternative to cow’s milk; published in the English language; present data on the serving size, ingredients, and nutritional composition, containing at least data on energy and macronutrients of plant-based beverages. Ingredients and data on energy, macronutrients, and, if available, dietary fiber and some micronutrients of plant-based beverages were collected. Data were obtained from 122 beverages of 22 different matrices, with soy being the most used (27.87%, n = 34). The variation in the amount of nutrients found was 6–183 Kcal/100 mL for energy value; 0.00–22.29 g/100 mL for carbohydrate; 0.06–12.43 g/100 mL for protein; 0.00–19.00 g/100 mL for lipid; 0.00–4.40 g/100 mL for dietary fiber; 0.00–1252.94 mg/100 mL for calcium; 0.04–1.40 mg/100 mL for iron; 0.84–10,178.60 mg/100 mL for magnesium; 0.00–343.43 mg/100 mL for sodium. Salt was the most commonly found added ingredient in plant-based beverages. Some beverages have reached certain amounts of cow’s milk nutrients. However, studies have pointed out differences in their qualities/types. Thus, attention is needed when replacing milk with these alternatives.