PLS-SEM Validation for Burnout Measures in Latino College Students: A Socially Sustainable Educational Return

Health care is an essential factor in the social sustainability of the university;therefore, it is a challenge and a responsibility to monitor a safe return to school that ensures the support of the physical and emotional well-being of students. In this sense, the Maslach Burnout Inventory-Student Survey (MBI-SS) is a validated resource with robust techniques in several regions of the world to diagnose school burnout. However, few efforts appear in the literature to validate it from a predictive approach in the Latin region. This study aims to validate, from a predictive approach, measures of school burnout in Latino university students from Mexico and Colombia. A total of 235 surveys were administered (Mx. n = 127, Co. n = 108), and a Partial Least Squares (PLS) measurement model was validated using the statistical program SmartPLS 3.3.7. As a result, 22 valid items were obtained in four reliable subconstructs: burnout, family cynicism, inefficacy, and somatization. The value of this research is its contribution to filling two gaps related to the MBI-SS scale (1) to contribute to the validation of the MBI-SS in a Latin context and (2) the use of the nonparametric statistical technique PLS focused on prediction.

Success Model Measuring Implementation of Customer Intention to use Virtual Booth on E-Commerce System in the Pandemic Covid-19

This study develops a virtual booth implementation measurement model in e-commerce to increase customer intention to use and then buy. This development is based on this pandemic period. The use of e-commerce has become a place of buying and selling, so new innovations in the system are needed to increase visitors to the e-commerce system to buy. Thus, in this study, we develop a success measurement model for implementing a system based on the IS success model developed by Delon and Mclean. This study uses the variables Information Quality, Service Quality, Exhibition experience, System Quality, exhibition Satisfaction, and intention to use. This variable can play a role in helping determine the effectiveness of the implemented information system. Furthermore, this research will be used to measure respondents' quantitative analysis using the system. © 2022 IEEE.

UNIVERSITY LECTURERS' PREPAREDNESS TO USE TECHNOLOGY IN TEACHER TRAINING OF MATHEMATICS DURING COVID-19: THE CASE OF ETHIOPIA

The study aimed to investigate Ethiopian university lecturers' readiness to use technology for teaching mathematics at the tertiary level during the COVID-19 pandemic when they were compelled to adapt to distant education. Using Google Forms, online questionnaires were distributed to 41 lecturers in three Ethiopian universities, of whom eighteen participated. Before the research, the questionnaire was piloted with eight lecturer participants to categorise questions and validate the instrument using the Rasch measurement model. The questionnaire was locally developed based on guidelines from the literature. It purposed to investigate university lecturers' individual preparedness for technological instruction in terms of their knowledge, beliefs and current, and historical exposure to this mode of instruction. As a counterbalance, some circumstantial factors influencing their readiness were investigated too. Lecturers' optimistic beliefs about using educational technologies have been found to contrast with some disabling circumstantial factors. This study revealed that the lecturers were generally able and interested in integrating technology into the teaching process but that barriers, primarily at the institutional level, hindered them from doing so. In addition to the technologies suggested in the questionnaire, participants enriched the research findings by adding more possible technologies that lecturers may use for educational purposes. The data was analysed using WINSTEPS (Student Version of WINSTEPS 4.7.0.0) and SPSS version 20. The results showed the reliability of using the instrument was 0.77 based on Cronbach's alpha. The PT-measure correlation value determined the construct validity (PMC), ranging from 0.23 to 0.71 except item PUT15's infit and outfit MNSQ between 0.1 to 1.86 and ZSTD range -1.05 to 1.61, which was acceptable. The fit statistics showed that the person separation index, 1.97, was considered good and that the item separation index, 0.63 was within an acceptable range. Person and item reliability were at 0.8 and 0.28, respectively. The result indicated that the new instrument with five items after eliminating unfit items (such as items FAT19, PTT10, KDT1, PTT8 and PTT 12) was reliable and valid to measure the use of technology in the teaching and learning process of the university lecturers.

Operating, financial and investment impacts of Covid-19 in SMEs: Public policy demands to sustainable recovery considering the economic sector moderating effect.

Currently, many institutions and academics are working to establish strategies of economic recovery with the aim of mitigating the short- and long-term impacts of the COVID-19 crisis. The main aim of this study is to analyze how this crisis has impacted Spanish SMEs, considering their operating, financial, and investment activities. We also analyze the initiatives or public policies that SME managers consider necessary in order to face the effects of COVID-19. To do this, an empirical study has been carried out based on information from 612 Spanish SMEs, estimating a PLS research model and multigroup analysis that considers the activity sector as a moderating variable. The results are useful to companies and different economic and social agents, providing information to facilitate decision-making to overcome pandemic crisis mainly in the economic and strategic spheres.

Structural Model of Metacognition in Online Learning during the Covid-19 Pandemic

During the Covid-19 pandemic crisis, the conventional learning process through face-to-face interactions between lecturers and students was suspended due to the implementation of the social restrictions policy by the government to control and reduce the spread of the coronavirus. At that time, the learning process was carried out online, the interaction between lecturers and students through internet communication media by utilizing applications via e-Iearning, google meetings, zoom, and so on. This study aims to develop a measurement model of self-efficacy, digital literacy, and metacognition in higher education and to develop a metacognitive structural model of undergraduate students in The Electrical Engineering Department (EED) Faculty of Engineering (FE) Unesa, and the structural model of metacognition of EED-FE Unesa students in online learning during the Covid-19 pandemic. The research design uses predictive methods to assess self-efficacy and digital literacy on metacognition in online learning. The instrument is validated by experts in the fields of vocational education, learning technology, and educational evaluation. Data collection techniques using survey methods with online communication. The research population is students of the EED-FE Unesa. The research sample was collected through stratified purposive sampling. The data analysis technique is a quantitative research approach that uses a structural equation model (SEM). The SEM design uses the analysis of two models, and there were the measurement model and the structural model. There are three latent variables of the measurement model, i.e., digital self-efficacy, digital literacy, and metacognition. The results showed that the measurement model was able to produce convergent validity, meaning that the items to measure the construct were valid. This means that the indicator variable is correlated with the latent variable. The structural model of metacognition fit with data. © 2021 IEEE.

Quality Evaluation of Image Dataset based on Label File

The quality of the dataset affects the accuracy of the artificial intelligence model, but it is a lot of work to manually detect errors related to the quality evaluation of the dataset, and it may not be possible to perform quality evaluation through simple viewing. Therefore, we propose an image dataset quality measurement model, including nine evaluation metrics, and analyze the evaluation metrics from three aspects: definition, calculation formula and description. Based on the label file, the quality of the dataset file and the content of the dataset is evaluated, and the evaluation standard is given to judge whether the quality of the dataset is qualified. The measurement model and evaluation criteria proposed in this article were verified against the Cifar-10 dataset and the COVID-CT dataset, and the problems of label accuracy and label category imbalance were found, which proved the effectiveness of the method in this paper. © 2021 IEEE.

Identify and measure the degree of over-prevention behaviors in the post-COVID-19 era in China.

BACKGROUND: With the spread of vaccines, more and more countries have controlled the outbreak of the COVID-19. In this post-epidemic era, these countries began to revive their economy. However, pollution remains in the environment, and people's physical and psychological health has been under threat due to some over-prevention behaviors. Instruments for governmental agencies to manage these behaviors are not yet available. This study aims to develop a measurement model to identify and measure the degree of over-prevention behaviors during the COVID-19 epidemic in China. METHODS: A survey online was conducted to collect cognition from 1528 Chinese people, including descriptions of various over-prevention behaviors defined by health authorities. Factor analyses were used to develop the measurement model and test its validity. Logistic regression analyses were conducted to explore demographic characteristics, indicating people who are inclined to exhibit over-prevention behaviors. RESULTS: Four main factors were extracted to develop the model (eigenvalue = 7.337, 3.157, 1.447, and 1.059, respectively). The overall reliability (Cronbach's α = 0.900), the convergent (AVE > 0.5, CR > 0.8 for each factor) and discriminant validity is good. There is also a good internal consistency among these factors (Cronbach's α = 0.906, 0.852, 0.882, and 0.763, respectively). In Factor 1, gender has a negative effect (Beta = - 0.294, P <  0.05, OR = 0.745), whereas employment has a positive effect. Workers in institutions exhibit the greatest effect (Beta = 0.855, P <  0.001, OR = 2.352). In Factor 2, employment has a negative effect, with workers in institutions exhibit the greatest role (Beta = - 0.963, P <  0.001, OR = 0.382). By contrast, education level has a positive effect (Beta = 0.430, P <  0.001, OR = 1.537). In Factor 3, age plays a negative role (Beta = - 0.128, P < 0.05, OR = 0.880). CONCLUSIONS: People show a discrepancy in the cognition toward various over-prevention behaviors. The findings may have implications for decision-makers to reduce the contradiction between the epidemic and economic revival via managing these behaviors.

The Fear of COVID-19 Scale: A Reliability Generalization Meta-Analysis.

A reliability generalization meta-analysis was carried out to estimate the average reliability of the seven-item, 5-point Likert-type Fear of COVID-19 Scale (FCV-19S), one of the most widespread scales developed around the COVID-19 pandemic. Different reliability coefficients from classical test theory and the Rasch Measurement Model were meta-analyzed, heterogeneity among the most reported reliability estimates was examined by searching for moderators, and a predictive model to estimate the expected reliability was proposed. At least one reliability estimate was available for a total of 44 independent samples out of 42 studies, being that Cronbach's alpha was most frequently reported. The coefficients exhibited pooled estimates ranging from .85 to .90. The moderator analyses led to a predictive model in which the standard deviation of scores explained 36.7% of the total variability among alpha coefficients. The FCV-19S has been shown to be consistently reliable regardless of the moderator variables examined.

A Country Pandemic Risk Exposure Measurement Model.

PURPOSE: The purpose of this study is to develop a Pandemic Risk Exposure Measurement (PREM) model to determine the factors that affect a country's prospective vulnerability to a pandemic risk exposure also considering the current COVID-19 pandemic. METHODS: To develop the model, drew up an inventory of possible factor variables that might expose a country's vulnerability to a pandemic such as COVID-19. This model was based on the analysis of existing literature and consultations with some experts and associations. To support the inventory of selected possible factor variables, we have conducted a survey with participants sampled from people working in a risk management environment carrying out a risk management function. The data were subjected to statistical analysis, specifically exploratory factor analysis and Cronbach Alpha to determine and group these factor variables and determine their reliability, respectively. This enabled the development of the PREM model. To eliminate possible bias, hierarchical regression analysis was carried out to examine the effect of the "Level of Experienced Hazard of the Participant (LEH)" considering also the "Level of Expertise and Knowledge about Risk and Risk Management (LEK)". RESULTS: Exploratory factor analysis loaded best on four factors from 19 variables: Demographic Features, Country's Activity Features, Economic Exposure and Societal Vulnerability (i.e. the PREM Model). This model explains 65.5% of the variance in the level of experienced hazard (LEH). Additionally, we determined that LEK explains only about 2% of the variance in LEH. CONCLUSION: The developed PREM model shows that monitoring of Demographic Features, Country's Activity Features, Economic Exposure and Societal Vulnerability can help a country to identify the possible impact of pandemic risk exposure and develop policies, strategies, regulations, etc., to help a country strengthen its capacity to meet the economic, social and in turn healthcare demands due to pandemic hazards such as COVID-19.