Relationship Between Green Innovation and Sustainability and Moderating Role of Foreigners: Evidence from Saudi Listed Companies

This paper investigates the effect of the presence of non-Saudi members on the board and committees and the relationship between green innovation and sustainability reporting. I rely on the ISO 26000 and CSR guidelines as an index of sustainability measures. Based on a sample of Saudi listed firms, for the period 2017–2020, I run a multiple regression to test the moderating role of the effect of foreigners' presence on the link between green innovation and sustainability reporting. The main issue of the paper is to address whether the presence of foreign members on the board of directors affects the firm's decision to voluntarily respond to major stakeholders' demands for increased sustainability reporting in Saudi Arabia. I found that the last appearance of COVID-19 affected the level of sustainability reporting, which led to the impact on the results of the relationship between the variables as well. By using a hierarchical moderated regression analysis, my findings confirm that green innovation has a crucial role in promoting sustainability, but the presence of foreigners in different committees has no effect on the relationship. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

COVID-19 Future Forecasting Using Machine Learning Models

Covid-19 is a very infectious virus. According to World Health Organization (WHO), millions of individuals have been diagnosed with Covid-19 since then, and at least a million have died as the virus has expanded dramatically. While most of the news on this front is scary, technology is helping to pave the path through this crisis. Manual forecasting is a difficult challenge for humans due to its large scale and complexity. Machine Learning (ML) techniques can effectively predict Covid-19 infected patients. There are a lot of study that have been developed to predict and forecast the future number of cases affected by Covid-19. In this area, our forecasting can be tackled as a problem of supervised learning. Supervised ML is very popular regression methods due to its simplicity to be interpreted by Humans. In this paper, we use two datasets to predict the symptoms through two different types of regression algorithms (single and multiple regression), the ML algorithms are LR, SVM, LASSO, ES and Polynomial regression, for the multiple regression we used LR, SVM and LASSO. The obtained results validate that for the single regression the Exponential Smoothing (ES) outperforms other machine learning approaches like Linear Regression (LR) and LASSO in terms of R-Square, Adjusted R-Square, Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The same accuracy is observed for the models used in the multiple regression. © 2022 IEEE.

7Ps of Service Marketing and Digital Media on Purchase Intention in Tourism

Indonesian tourism industry is facing decline since Covid-19 pandemic. International tourists dropped from 16 million in 2019 to 1.5 million in 2021. Tourism businesses must increase their number of tourists in order to survive. One of the solution is to attract more domestic tourists. Previous researches showed that 7Ps of Service Marketing and Digital Media have significant effect on Purchase Intention. 300 samples were gathered from Java and Bali Island and tested using multiple regression. Result showed that 7Ps of Service Marketing has positive and significant effect on Purchase Intention. Digital Media also has positive and significant effect on Purchase Intention. The most important factors in 7Ps are people and physical evidence while the most important factors in Digital Media are information and design. Indonesian people are very social and feel more at ease when there are physical evidence and human interaction on their vacation destination. Domestic tourists also prefer clear information on digital media and fond of appealing design in digital media. These findings encourage tourism business to increase their digital media design and provide more information on their media, tourism business also gain advantage from presenting good physical evidence and providing pleasant human interaction. © 2022 ACM.

Research on the Spread and Influencing Factors of the COVID-19 Variant in the UK

This paper first establishes a clustering algorithm to cluster the data in the attachment, dividing the data into three categories, and determines the infection rate and transmission time of each virus. On this basis, the cities in four regions are selected for analysis, and a multiple regression fitting model is established to determine their corresponding transmission in different stages and regions. In order to better analyze, this paper takes Aberdeen City as an example. According to the data corresponding to the original three viruses, a grey correlation model is established to analyze the data. Finally, according to the number of confirmed cases of Omicron virus transmission, this paper estimates the duration of Omicron virus transmission by establishing a neural network prediction model. © 2022 IEEE.

Virtual Internships as Employer-Led Initiatives: Success Criteria and Reflections on the Diversification of Internships

Many employers have trialed virtual internships over the past two years. Employer-led virtual internships (e-internships) have a long history that predates the Covid-19 pandemic. Previous research has already demonstrated the importance of how employers design their internships, and how they subsequently support, train and mentor interns for internship success. Fifty-one virtual interns completed a survey in 2020 about their virtual internships experience with employers in various countries. The survey examined predictors of internship satisfaction and usefulness. Multiple regression showed that information accuracy, perceived support (e.g., resources, access to help) and usefulness (in terms of knowledge advancement and consolidation) all significantly and positively predicted internship satisfaction. Social influence and perceived support also increased the ratings of perceived usefulness among virtual interns. The relationship between perceived support given by employers and internship satisfaction was partially mediated by perceived usefulness of the internship. This highlights the importance of employer-led provisions and the extent to the design of internships influence virtual interns’ subsequent evaluations. The study concludes with a discussion of practical implications and reflections on the need to differentiate and study the various virtual internship types that have appeared to analyze which one will add value and which types might be less beneficial or even exploitative of talent wishing to gain more experience via virtual internships. © 2022, IFIP International Federation for Information Processing.

Determinants for Adoption of MOOCs from the Perspective of UTAUT

The growth of information and communication technology (ICT) coupled with cheaper internet charges and android and windows-based computing devices facilitate online learning. The scaling up of the online teaching and learning process helping both educators and learners has happened during covid-19. Students have been using various platforms for online learning according to their level of understanding and as per instructions given by their institutes. The present study aims to examine the adequacy of the Unified Theory of Acceptance and Use of Technology (UTAUT) as a base theory in an academic context for identification of the factors which impact academicians and students' behavioral intentions in the adoption of MOOCs. Studies have been conducted in developed countries but a detailed study where students' have undergone sufficient exposure to remote learning is now worthwhile. The cross-sectional study used a descriptive methodology to test hypotheses related to the interaction between dependent variable behavioral intention of UTAUT, with independent variables like facilitating conditions (FC), performance expectancy (PE), social influence (SI), and effort expectancy (EE). Data was gathered through a self-administered questionnaire designed using Questionpro. For data analysis, multiple linear regression (MLR) was carried out using SPSS software V23. The research model was found significant with an explanatory power of 39.8 %. The study found that PE and EE were significant influencers to academicians' and students' behavioral intention for MOOCs. SI and FC, on the other hand, were not found to be significant predictors. © 2022 IEEE.

Assessment of the Impact of Personality Characteristic on Emotion Resilience

This study assesses emotional resilience (ER) among people residing in the eastern region of India based on six demographic factors age, gender, education, marital status, working hours and financial status. MATERIAL/METHODS: Researchers conducted a survey through questionnaire among people aged 25 and 75 (mean age =50, mathrm{N}=508) from 1 September 2021 to 20 October 2021 after two COVID-19 outbreaks and analysed the data using the chi-square test, Cronbach alpha, multiple regression (MR) and logistic regression (LR). RESULTS: Results of present research show that ER negatively correlates with age (coeff. =14.315, mathrm{p}=5.298mathrm{E}-11) and positively with financial status (mathrm{t}=8.924, mathrm{p}=1.876mathrm{E}-09). The three personality characteristics impacting ER most were: 1. I am playful. I find humour in rough situations and can laugh at myself. 2. I am curious. I ask questions. I want to know how things work. I like to try new ways of doing things. 3. I learn valuable lessons from my experiences and the experiences of others. CONCLUSION: Of six demographic variables, only two (age and financial status) correlated with ER. © 2022 IEEE.

A Nonlinear Transformation Methods Using Covid-19 Data in the Kurdistan Region

Ordinary Least squares (OLS) are the most widely used due to tradition and their optimal properties to estimate the parameters of linear and nonlinear regression models. Nevertheless, in the presence of outliers in the data, estimates of OLS become inefficient, and even a single unusual point can have a significant impact on the estimation of parameters. In the presence of outliers is the use of robust estimators rather than the method of OLS. They are finding a suitable nonlinear transformation to reduce anomalies, including non-Additivity, heteroscedasticity, and non-normality in multiple nonlinear regression. It might be beneficial to transform the response variable or predictor variable, or both together to present the equation in a simple, functional form that is linear in the transformed variables. To illustrate the superior transformation function, we compare the squared correlation coefficient (coefficient of determination), Breusch-Pagan test, and Shapiro-Wilk test between the transformation functions. © 2022 IEEE.