Drawing on the theoretical framework of Job Demands-Resources (JD-R), our study aims to consider how workplace antecedents of perceived quiet firing (also known as involuntary attrition), perceived co-worker support, and experience (tenure at an organization) may influence quiet quitting behavior. Data were collected via questionnaire responses from 209 workers in India who had graduated from university within the last 7 years. The findings show that (1) perceived quiet firing is positively associated with quiet quitting; (2) perceived co-worker support is negatively associated with quiet quitting; (3) experience moderates the positive association between perceived quiet firing and quiet quitting in such a way that the relationship is weaker as one’s tenure at an organization increases; and (4) experience does not moderate the negative association between perceived co-worker support and quiet quitting. The study’s contributions come from understanding how the interplay of demands (i.e., perceived quiet firing) and resources (i.e., perceived co-worker support and experience) determine quiet quitting behaviors in the workplace. Additionally, the temporal dimension of experience facilitates the acquisition of organizational-specific knowledge and resources. In contrast, perceptions of co-worker support appear specific to a given point in time. Policy implications come from providing guidance to organizations on how to reduce quiet quitting behaviors by ensuring that the resources available to employees exceed the demands placed on them.

One significant importance of street vending in South Africa is its role in providing livelihoods and economic opportunities, especially for marginalized and vulnerable populations. However, Street vendors, particularly those selling agricultural commodities, face numerous challenges. Street vending in Moletjie Mmotong is a vital source of income and employment, offering affordable goods and services, including food, clothing, and household items. One potential solution is online selling, but there is limited knowledge about it in the informal sector. This study aims to analyze the factors affecting street vendors’ willingness to sell fruits and vegetables online in Moletjie Mmotong under Polokwane Municipality. Data was collected from 60 street vendors using a questionnaire and simple random sampling. Descriptive statistics identified and described the socio-economic characteristics of the vendors, while a binary logistic regression model analyzed the factors influencing their willingness to sell online. The study found that age, education level, gender, household size, and access to online selling information significantly influenced their willingness to sell online. The findings highlight the potential benefits of online selling for street vendors, such as increased sales and a broader customer base. The study recommends that governments provide training and workshops on online selling, develop educational programs, distribute educational materials, and create marketing strategies to support street vendors in transitioning to online platforms.

Disaster Risk Management benefits from innovative techniques including AI and Multi Sensor Fusion. The Firefguard Approach uses such technologies to improve the Wildfire Management works in Saxony, Eastern Germany by supporting standing efforts in Early Warning, Disaster Response and Monitoring. Unmanned Aerial Systems (UAS) play a vital role in providing real-time information via a 5G network to a central information management system that delivers geospatial information to response teams. This study highlights the potential of combining UAS, AI, geospatial solutions and existing data for real-time wildfire monitoring and risk assessment systems.

While the healthcare landscape continues to evolve, rural-based hospitals face unique challenges in providing quality patient care amidst resource constraints and geographical isolation. This study evaluates the impact of big data analytics in rural-based hospitals in relation to service delivery and shaping future policies. Evaluating the impact of big data analytics in rural-based hospitals will assist in discovering the benefits and challenges pertinent to this hospital. The study employs a positivist paradigm to quantitatively analyze collected data from rural-based hospital professionals from the Information Technology (IT) departments. Through a comprehensive evaluation of big data analytics, this study seeks to provide valuable insights into the feasibility, infrastructure, policies, development, benefits and challenges associated with incorporating big data analytics into rural-based hospitals for day-to-day operations. The findings are expected to contribute to the ongoing discourse on healthcare innovation, particularly in rural-based hospitals and inform strategies for optimizing the implementation and use of big data analytics to improve patient care, decision-making, operations and healthcare sustainability in rural-based hospitals.