The benefits of information system users are an important topic in research on information system implementation in general as well as in hospital information systems in particular. The study is applying structural equation modelling in determining the factors affecting personal benefits of information system users, with the antecedents being the combination of perspectives, and the outcomes including individual user results of the system in hospitals. The study was conducted in two phases: a preliminary study and a formal study. The preliminary study aimed to adjust and supplement the observed variables to be suitable for the actual conditions in Vietnam by conducting a preliminary survey with a questionnaire involving 55 samples to assess the internal consistency reliability, convergent validity, and discriminant validity of the measurement scales. The formal quantitative study, which employed linear structural analysis with PLS-SEM, was conducted on 215 samples of individuals who had previously used information systems in several hospitals in Vietnam. The proposed model explained 80.6% of the variance in user engagement with the system and 50.6% of the variance in user satisfaction when using the information system. In more detail, for user benefits, it is worth noting that the strongest impact intensity belongs to information quality and the weakest belongs to support structure. In addition, confidence in one’s own abilities also has a high impact on user benefits when using the information system.

Agriculture is a determining factor regarding the development of the Romanian economy, noting its importance for population consumption and as a supplier of raw materials for the relaunch of other industries. Agricultural financing consists of credits granted to natural or legal persons for developing agricultural activities, expanding agricultural holdings, and commercializing agricultural production. The objective of this research is the statistical analysis of the determining factors in granting loans to Romanian farms. The study is based on the content analysis of the accounting reports of the 45 Romanian farms included in the research sample, based on which the profile of the farmer from the selected counties (Alba, Cluj, Mures, Sibiu, Dambovita and Prahova) is outlined. The obtained results highlight the fact that factors such as the requested amount (SUSO) are directly influenced by the worked area (TELU), by the turnover (CIAF), R = 0.6228, but also by the total value of the assets (TOTAL) R = 0.454. At the opposite pole, there is a weak correlation between SUSO and current liquidity (LICU), R = 0.2754, and the value of recorded expenses (CHEL), R = 0.3102. Implementing a credit policy that facilitates access to financing sources would support farms in modernization and development, increasing their competitiveness and general viability.

In rural areas, land use activities around primary arterial roads influence the road section’s traffic characteristics. Regulations dictate the design of primary arterial roads to accommodate high speeds. Hence, there is a mix of traffic between high-speed vehicles and vulnerable road users (pedestrians, bicycles, and motorcycles) around the land. As a result, researchers have identified several arterial roads in Indonesia as accident-prone areas. Therefore, to improve the road user’s safety on primary arterial roads, it is necessary to develop models of the influence of various factors on road traffic accidents. This research uses binary logistic regression analysis. The independent variables are carelessness, disorderliness, high speed, horizontal alignment, road width, clear zone, road shoulder width, signs, markings, and land use. Meanwhile, the dependent variable is the frequency of accidents, where the frequency of accidents consists of multi-accident vehicles (MAV) and single-accident vehicles (SAV). This study collects data for a traffic accident prediction model based on collision frequency in accident-prone areas. The results, road shoulder width, and road sign factor all have an impact on the frequency of traffic accidents. According to a realistic risk analysis, MAV and SAV have no risk difference. After validation, this model shows a confidence level of 92%. This demonstrates that the model generates estimations that accurately reflect reality and are applicable to a wider population. This research has the potential to assist engineers in improving road safety on primary arterial roads. In addition, the model can help the government measure the impact of implemented policies and engage the public in traffic accident prevention efforts.

It is increasingly obvious the huge improvement caused in loss of habitat and degradation in environment. Various nations are prone to natural disasters if this issue is not addressed. The development of finance has been hailed as significant in alleviating environmental concerns due to its part as a source of cash for the development of green technology. The primary goal of this research is to satisfy an acquaintance vacuum by investigating the relationship amongst economic growth and ESG (Environmental, Social and Governance) concert throughout Asia. This analysis made use of country-level data from 2010 to 2015. Economic growth is positively connected to ESG routine, due to examination upon the pooled normal least squares method, the immovable impact logistic method, these two-phase least squares technique, and the structure’s generalised approach of moments estimator. Additionally, additional tests including financial sector growth subcomponents (financial platforms and financial institutions) reveal that the conclusion is consistent and resilient under multiple model settings. Financial development, when combined, is an essential catalyst for promoting ESG performance in Asia.

This study examines factors associated with an increasingly poor perception of the novel coronavirus in Africa using a designed electronic questionnaire to collect perception-based information from participants across Africa from twenty-one African countries (and from all five regions of Africa) between 1 and 25 February 2022. The study received 66.7% of responses from West Africa, 12.7% from Central Africa, 4.6% from Southern Africa, 15% from East Africa, and 1% from North Africa. The majority of the participants are Nigerians (56%), 14.1% are Cameroonians, 8.7% are Ghanaians, 9.3% are Kenyans, 2% are South Africans, 2.1% are DR-Congolese, 1.6% are Tanzanians, 1.2% are Rwandans, 0.4% are Burundians, and others are Botswana’s, Chadians, Comoros, Congolese, Gambians, Malawians, South Sudanese, Sierra Leoneans, Ugandans, Zambians, and Zimbabweans. All responses were coded on a five-point Likert scale. The study adopts descriptive statistics, principal component analysis, and binary logistic regression analysis for the data analysis. The descriptive analysis of the study shows that the level of ignorance or poor “perception” of COVID-19 in Africa is very high (87% of individuals sampled). It leads to skepticism towards complying with preventive measures as advised by the WHO and directed by the national government across Africa. We adopted logistic regression analysis to identify the factors associated with a poor perception of the virus in Africa. The study finds that religion (belief or faith) and media misinformation are the two leading significant causes of ignorance or poor “perception” of COVID-19 in Africa, with log odd of 0.4775 (resulting in 1.6120 odd ratios) and 1.3155 (resulting in 3.7265 odd ratios), respectively. The study concludes that if the poor attitude or perception towards complying with the preventive measures continues, COVID-19 cases in Africa may increase beyond the current spread.