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This study investigated the factors determining the behavioral intention of young Saudi entrepreneurs to use crowdfunding to finance their small enterprises. The capacity of behavioral intention to predict the use behavior of crowdfunding platforms was also assessed. Partial least squares-based structural equation modeling method (PLS-SEM) was used to analyze responses collected from 270 young Saudi entrepreneurs (qualified students) attending the Community College of Abqaiq in Saudi Arabia, which is affiliated with King Faisal University. The unified theory of acceptance and use of technology (UTAUT) with extensions of three constructs was employed. The findings revealed that performance expectancy, social influence, perceived trust and perceived risks predicted the behavioral intention of young Saudi entrepreneurs to use crowdfunding platforms to finance their small enterprises, whereas effort expectancy, facilitating conditions and trialability had no significant predictive effect on the same behavioral intention. It was further reported that behavioral intention could also predict the use behavior of young Saudi entrepreneurs on crowdfunding platforms. The overall results indicated that the model explained 55.4% of the variance in behavioral intention and 38.3% of the variance in use behavior of young Saudi entrepreneurs.

Chronic diseases represent a serious threat to public health across the world. It is estimated at about 60% of all deaths worldwide and approximately 43% of the global burden of chronic diseases. Thus, the analysis of the healthcare data has helped health officials, patients, and healthcare communities to perform early detection for those diseases. Extracting the patterns from healthcare data has helped the healthcare communities to obtain complete medical data for the purpose of diagnosis. The objective of the present research work is presented to improve the surveillance detection system for chronic diseases, which is used for the protection of people’s lives. For this purpose, the proposed system has been developed to enhance the detection of chronic disease by using machine learning algorithms. The standard data related to chronic diseases have been collected from various worldwide resources. In healthcare data, special chronic diseases include ambiguous objects of the class. Therefore, the presence of ambiguous objects shows the availability of traits involving two or more classes, which reduces the accuracy of the machine learning algorithms. The novelty of the current research work lies in the assumption that demonstrates the noncrisp Rough K-means (RKM) clustering for figuring out the ambiguity in chronic disease dataset to improve the performance of the system. The RKM algorithm has clustered data into two sets, namely, the upper approximation and lower approximation. The objects belonging to the upper approximation are favourable objects, whereas the ones belonging to the lower approximation are excluded and identified as ambiguous. These ambiguous objects have been excluded to improve the machine learning algorithms. The machine learning algorithms, namely, naïve Bayes (NB), support vector machine (SVM), K-nearest neighbors (KNN), and random forest tree, are presented and compared. The chronic disease data are obtained from the machine learning repository and Kaggle to test and evaluate the proposed model. The experimental results demonstrate that the proposed system is successfully employed for the diagnosis of chronic diseases. The proposed model achieved the best results with naive Bayes with RKM for the classification of diabetic disease (80.55%), whereas SVM with RKM for the classification of kidney disease achieved 100% and SVM with RKM for the classification of cancer disease achieved 97.53 with respect to accuracy metric. The performance measures, such as accuracy, sensitivity, specificity, precision, and F-score, are employed to evaluate the performance of the proposed system. Furthermore, evaluation and comparison of the proposed system with the existing machine learning algorithms are presented. Finally, the proposed system has enhanced the performance of machine learning algorithms.

The study investigates the impact of psychological capital on the employees’ innovative behavior through the mediating effect of employees’ job satisfaction and employees’ innovative intention in the small and medium enterprises (SMEs) sector of Saudi Arabia. A sample of 204 respondents participated from various enterprises working without restricting specific sectors to check employees’ common behavior in multiple sectors. The data and hypotheses testing analysis were made with the partial least squares–based structural equation modeling (PLS-SEM). The study revealed that psychological capital positively affects employees’ job satisfaction, innovative behavior, and innovative intention. Furthermore, the employees’ job satisfaction also positively correlated with the employees’ innovative behavior, while there was no connection between the employees’ innovative intention and the employees’ innovative behavior. Concerning the indirect relationships, the findings revealed that employees’ job satisfaction played a partial mediating role between psychological capital and the employees’ innovative behavior. However, the employees’ innovative intention did not mediate the relationship between the psychological capital and the employees’ innovative behavior. These findings suggest the importance of psychological capital in influencing the innovative behavior of employees. Hence, there is a need to continue developing it among employees to ensure a better output.

This study’s objective is to examine the influence of entrepreneurial self-efficacy and internal locus of control on the entrepreneurial intention of small Saudi entrepreneurs during adverse times, with entrepreneurial resilience as a moderator. The study, which targeted a sample of 207 small entrepreneurs working in various sectors in Saudi Arabia, gathered data through an online questionnaire sent to respondents and analysed the results using PLS-SEM. The study revealed intriguing findings, such as the existence of a positive significant relationship between entrepreneurial self-efficacy, internal locus of control and entrepreneurial intention amongst small Saudi entrepreneurs. It also demonstrated that in times of adversity, such as during the COVID-19 pandemic and other environmental challenges, entrepreneurial resilience can act as a moderator between entrepreneurial intention and entrepreneurial self-efficacy. Entrepreneurial resilience, in particular, has the potential to strengthen the relationship between entrepreneurial self-efficacy and entrepreneurial intention. Accordingly, the government, along with other sectors and stakeholders in Saudi Arabia, should continue to support the psychological characteristics of small Saudi entrepreneurs, notably their internal locus of control, entrepreneurial self-efficacy, and entrepreneurial resilience to ensure greater sustainability and the continuity of their small businesses.