The primary objective of this research is to investigate how non-financial incentives impact employee motivation within the Small and Medium Enterprises (SMEs) operating in Saudi Arabia. Employing a positivist research approach, we employed a carefully crafted survey to collect data from 365 employees employed by SMEs situated in Jeddah. The study explores various aspects, including the most common non-monetary motivators, the interplay between non-monetary and monetary incentives, and the effects of non-financial incentives on employee engagement, job satisfaction, and commitment. The results of the study indicate that employees working in small and medium-sized enterprises (SMEs) in Saudi Arabia place a significant emphasis on a good work environment, recognition, possibilities for personal and professional development, and career growth as prevalent non-monetary motivators. Additionally, the research illustrates a notable difference in the perceived efficacy of non-financial and financial incentives, whereby non-financial incentives are seen to have an equal, if not greater, impact on both motivation and work satisfaction. Moreover, the study reveals robust positive correlations between non-financial incentives and employee outcomes, underscoring the significance of these incentives in augmenting work satisfaction, job engagement, and commitment. The consequences of employee motivation are influenced by control factors, which have diverse influences, highlighting the complex nature of this phenomenon.

The use of infrastructure as a catalyst for Indonesia’s economic growth faces significant challenges. One example is the construction projects, which have not reached the intended goal and have led to an increase in investment cost compared to the original plan. Additionally, the interaction between the government and companies involved in toll-road construction projects under the public-private partnerships (PPP) mechanism has yet to produce good quality project governance and expected project performance. This study aimed to find empirical data on the determination of project intellectual capital and project ownership structure through good project governance on toll-road project performance in Indonesia. This study adopted a quantitative approach that involved data collected through a survey conducted among toll-road projects from 2015 to 2019. The data was analyzed with Structural Equation Modeling Partial Least Square (SEM-PLS). The results showed that project intellectual capital and project ownership structure significantly affected good project governance. Good project governance Practices significantly affected project performance. Project intellectual capital and project ownership structure influenced project performance through the mediation of good project governance. Conversely, two hypotheses were not supported by the data, i.e., the effect of project intellectual capital and project ownership structure on project performance. The findings of this research contributed to the literature regarding the implementation of collaborative governance in PPPs toll road development projects in Indonesia by providing a framework and assessment tools, which could be valuable for researchers and policymakers in analyzing and evaluating the governance and performance of toll road construction PPP projects.

In response to the rapid and dynamic changes in the economic environment, companies must improve their processes to maintain competitiveness. This includes enhancing their intellectual capital, with particular emphasis on effective onboarding processes, which play a crucial role in integrating new employees and retaining talent. This enhances the value of the organization’s intellectual capital and emphasizes onboarding—the training and integration of new employees—whose proper functioning impacts staff retention. Drawing on both Hungarian and predominantly foreign literature, we highlight onboarding processes and examine their implementation in Hungarian companies of various sizes. The research employed a mixed-method approach, combining semi-structured interviews and questionnaires. In-depth interviews were conducted with HR leaders from 13 Hungarian organizations to explore the existence of mentoring programs. Additionally, 161 employees across Hungary completed questionnaires, which examined their perspectives on onboarding processes and the relationship between mentoring programs and company size. We analyzed the data using chi-square tests to assess the strength of these relationships. While all large companies in our sample had formal mentoring programs, smaller companies displayed more variability, with some relying on informal or ad-hoc onboarding processes. Based on these results, we identified several key areas for improvement in onboarding processes. These include enhancing the structure of feedback interviews, ensuring more comprehensive communication channels, and strengthening mentoring programs across companies of all sizes. By addressing these gaps, companies can improve employee retention, engagement, and overall integration during the onboarding process, contributing to a more stable and motivated workforce.

This research explores the advancement of Artificial Intelligence (AI) in Occupational Health and Safety (OHS) across high-risk industries, highlighting its pivotal role in mitigating the global incidence of occupational incidents and diseases, which result in approximately 2.3 million fatalities annually. Traditional OHS practices often fall short in completely preventing workplace incidents, primarily due to limitations in human-operated risk assessments and management. The integration of AI technologies has been instrumental in automating hazardous tasks, enhancing real-time monitoring, and improving decision-making through comprehensive data analysis. Specific AI applications discussed include drones and robots for risky operations, computer vision for environmental monitoring, and predictive analytics to pre-empt potential hazards. Additionally, AI-driven simulations are enhancing training protocols, significantly improving both the safety and efficiency of workers. Various studies supporting the effectiveness of these AI applications indicate marked improvements in risk management and incident prevention. By transitioning from reactive to proactive safety measures, the implementation of AI in OHS represents a transformative approach, aiming to substantially reduce the global burden of occupational injuries and fatalities in high-risk sectors.

Biomass production (BIO) and its anomalies were modeled using MODIS satellite images and gridded weather data to test an environmental monitoring system in the biomes Atlantic Forest (AF) and Caatinga (CT) within SEALBA, an agricultural growing region bordered by the states of Sergipe (SE), Alagoas (AL), and Bahia (BA), Northeast Brazil. Spatial and temporal variations on BIO between these biomes were strongly identified, with the annual long-term averages (2007–2023) for AF and CT of 78 ± 22 and 58 ± 17 kg ha−1 d−1, respectively. BIO anomalies were detected through its standardized indexes—STD (BIOSTD), comparing the results for the years from 2020 to 2023 with the long-term rates from 2007 to each of these years. The highest negative BIOSTD values were in 2023, but concentrated in CT, indicating periods with the lowest vegetation growth, regarding the long-term conditions from 2007 to 2023. The largest positive BIOSTD values were for the AF biome in 2022, evidencing the highest vegetative vigor in comparison with the long-term period 2007–2022. The proposed BIO monitoring system is important for environmental policies as they picture suitable periods and places for agricultural and forestry explorations, allowing sustainable managements under climate and land-use changes conditions, with possibilities for replication of the methods in other environmental conditions.

Solar energy is a reliable and abundant resource for both heating and power generation. The current research examines how the novel class of nano-embedded Bees wax phase change materials (NEBPCMs) improves heat storage qualities. The synthetic NEBPCMs were subjected to experimental testing using, XRD, Bees wax and Al2O3 FESEM. A typical solar water heating system features a flat plate collector unit incorporating Bees Wax phase change material (NEBPCM) combined with varying concentrations of Al2O3 (0.01%, 0.015%, and 0.02%). The absorber plate surface is coated with a Nano-hybrid coating consisting of Black Paint, Al2O3, and additional Fe3O4 at a 2% concentration. Pure water is frequently used in these solar water heaters (SWH), with performance evaluations conducted using different Bees Wax and Al2O3 concentrations of NEBPCM (Bees Wax + Al2O3). The system’s efficiency is assessed across different flow rates (60, 90, and 120 kg/hr) and tilt angles (15, 30, and 45 degrees). This study aims to examine the feasibility of using PCMs to store solar energy for night time water heating, ensuring a continuous supply of hot water maximum efficiency achieved by using NEBPCM in solar water heater 52.26% at a flow rate of 120 Kg/hr, at angle of 45 degrees and Concentration 0.015%.