Analysis of factors influencing public employees' work cognition under a public health crisis: A survey of China's response to COVID-19

A PLS-SEM approach to determine farmers' awareness about climate change mitigation and adaptation strategies: pathway toward sustainable environment and agricultural productivity

This research was conducted in a significant agricultural region to gauge farmers' knowledge of climate change adaption strategies. We employed a semi-structured questionnaire based on the literature; it was broken up into sections, and used certain statistical techniques (PLS-SEM) to examine the results. Farmers who had sufficient assets and resources thought they were safer and could withstand the adverse effects of climate change. A total of 900 completed questionnaires were gathered to investigate the link between the control, moderator, and DV variables in the future. As a consequence, the PLS-SEM path analysis findings showed that our model is fit. PLS-SEM direct path analysis revealed AM > FACC, UA- > FACC, SA- > FACC, FS- > FACC, PR- > FACC, and SI- > FACC are significant. The established hypotheses H1-H6 are strengthened by these findings. We also examined the respondents' ages and genders to use as controls; whereas gender showed no correlation with FACC, there was a strong link between age and the dependent variable. There is no statistically significant correlation between gender and climate change awareness, but older people tend to have a broader understanding of the topic and its consequences. Education significantly moderates the relationship of farmer's awareness (climate change) associated with AM, UA, SA, FS, PR, and SI. depicts the moderation role of education on the relationship between AM*Education- > FACC, UA*Education- > FACC, SA*Education- > FACC, FS*Education- > FACC, PR*Education- > FACC, and SI*Education- > FACC. H2a and H5a in this study showed significant correlations with education as a moderator; however, H1a, H3a, H4, and H6a did not demonstrate any moderator relationships. There is a medium to strong correlation between various factors, and the correlation values of a few chosen variables are significant when compared to all other variables in the current study. Highly significant correlations were found between PR, SA, SI, and UA with FACC. Governmental policies and effective monitoring systems will be developed as a result of the research to enable integrated and sustainable water development.

An SEM-ANN approach to evaluate public awareness about COVID, A pathway toward adaptation effective strategies for sustainable development

This study was conducted to evaluate public awareness about COVID with aimed to check public strategies against COVID-19. A semi structured questionnaire was collected and the data was analyzed using some statistical tools (PLS-SEM) and artificial neural networks (ANN). We started by looking at the known causal linkages between the different variables to see if they matched up with the hypotheses that had been proposed. Next, for this reason, we ran a 5,000-sample bootstrapping test to assess how strongly our findings corroborated the null hypothesis. PLS-SEM direct path analysis revealed HRP -> PA-COVID, HI -> PA-COVID, MU -> PA-COVID, PM -> PA-COVID, SD -> PA-COVID. These findings provide credence to the acceptance of hypotheses H1, H3, and H5, but reject hypothesis H2. We have also examined control factors such as respondents' age, gender, and level of education. Age was found to have a positive correlation with PA-COVID, while mean gender and education level were found to not correlate at all with PA-COVID. However, age can be a useful control variable, as a more seasoned individual is likely to have a better understanding of COVID and its effects on independent variables. Study results revealed a small moderation effect in the relationships between understudy independent and dependent variables. Education significantly moderates the relationship of PA-COVID associated with MU, PH, SD, RP, PM, PA-COVID, depicts the moderation role of education on the relationship between MU^*Education->PA-COVID, HI^*Education->PA.COVID, SD^*Education->PA.COVID, HRP^*Education->PA.COVID, PM^*Education -> PA.COVID. The artificial neural network (ANN) model we've developed for spreading information about COVID-19 (PA-COVID) follows in the footsteps of previous studies. The root means the square of the errors (RMSE). Validity measures how well a model can predict a certain result. With RMSE values of 0.424 for training and 0.394 for testing, we observed that our ANN model for public awareness of COVID-19 (PA-COVID) had a strong predictive ability. Based on the sensitivity analysis results, we determined that PA. COVID had the highest relative normalized relevance for our sample (100%). These factors were then followed by MU (54.6%), HI (11.1%), SD (100.0%), HRP (28.5%), and PM (64.6%) were likewise shown to be the least important factors for consumers in developing countries struggling with diseases caused by contaminated water. In addition, a specific approach was used to construct a goodness-of-fit coefficient to evaluate the performance of the ANN models. The study will aid in the implementation of effective monitoring and public policies to promote the health of local people.