Environmentally Specific Servant Leadership and Brand Citizenship Behavior: The Role of Green-Crafting Behavior and Employee-Perceived Meaningful Work.

Sustainability and environmental concerns have become increasingly important in the business world, with organizations seeking to integrate sustainable practices and enhance their brand citizenship behavior. Servant leadership that is focused on the environment is a type of leadership approach that gives prominence to preserving and promoting environmental sustainability. This study aims to examine the impact of environmentally specific servant leadership on brand citizenship behavior, with a focus on the mediating roles of green-crafting behavior and employee-identified meaningful work. Drawing on data from a survey of 319 employees working in hotels, this study conducted partial least square-structural equation modeling (PLS-SEM) to test a dual-moderated mediation model to explore the direct and indirect effects of environmentally specific servant leadership on brand citizenship behavior. The results of this study reveal that environmentally specific servant leadership has a significant and positive impact on green-crafting behavior and employee meaningful work. Moreover, green-crafting behavior and employee-perceived meaningful work both mediate the link between environmentally specific servant leadership and brand citizenship behavior. Specifically, green-crafting behavior acts as a mediator between environmentally specific servant leadership and employee-perceived meaningful work, while employee-perceived meaningful work mediates the link between green-crafting behavior and brand citizenship behavior. These findings have important implications for managers and organizations that seek to enhance their sustainability and brand citizenship behavior. Specifically, this study highlights the critical role of environmentally specific servant leadership (ESSL) in promoting green-crafting behavior and employee-perceived meaningful work, which in turn influence brand citizenship behavior. Therefore, organizations can improve their brand citizenship performance by developing ESSL behaviors and practices that foster green-crafting behavior and employee-perceived meaningful work.

Factors influencing entrepreneurial intention of university students in Yemen: The mediating role of entrepreneurial self-efficacy.

While entrepreneurship is believed to play a crucial role in economic growth and job creation in various parts of the world, particularly in developed countries, the key factors enhancing entrepreneurship behavior and intention in developing countries still need to be discovered. Therefore, this study examines the influence of personality traits and environmental and situational factors on the development of entrepreneurial intention among young students in Yemen. Data were collected through a survey responded to by 487 final-year university students from two universities (public and private) in Yemen. The study's hypotheses were tested using structural equation modeling (SEM). The study reveals that personality traits of the need for achievement (nAch) and locus of control (LoC) positively correlate with entrepreneurial self-efficacy (ESE) and entrepreneurial intention. Instrumental readiness positively correlates with ESE but not with entrepreneurial intent. The situational factors show a positive association with entrepreneurial intention but not ESE and a positive relationship between ESE and entrepreneurial intention. Furthermore, the study's findings show that ESE partially mediates the relationship between the nAch, LoC, instrumental readiness, and entrepreneurial intention. However, ESE did not mediate the relationship between situational factors and entrepreneurial intention. The study suggests that situational factors can influence entrepreneurial intention among Yemeni students and provide several recommendations to academicians and policymakers.

The roles of financial literacy and overconfidence in investment decisions in Saudi Arabia.

Financial literacy has gained much attention amongst scholars, policymakers and other stakeholders due to its role in backing up investment decisions, improving personal financial management and increasing financial wellbeing. This study examines the influence of financial literacy on investment decisions with the moderating effect of the overconfidence behavioural bias. Data were collected from 180 respondents in Saudi Arabia using a questionnaire, and a convenience sampling technique was applied. The study's findings were analysed using the partial least squares structural equation modelling (PLS-SEM) technique. It was found that financial literacy positively and significantly influenced investment decisions. Moreover, the results show that overconfidence positively affected investment decisions and that the relationship between financial literacy and investment decisions was positively and significantly moderated by overconfidence.

Detecting and Analyzing Suicidal Ideation on Social Media Using Deep Learning and Machine Learning Models.

Individuals who suffer from suicidal ideation frequently express their views and ideas on social media. Thus, several studies found that people who are contemplating suicide can be identified by analyzing social media posts. However, finding and comprehending patterns of suicidal ideation represent a challenging task. Therefore, it is essential to develop a machine learning system for automated early detection of suicidal ideation or any abrupt changes in a user's behavior by analyzing his or her posts on social media. In this paper, we propose a methodology based on experimental research for building a suicidal ideation detection system using publicly available Reddit datasets, word-embedding approaches, such as TF-IDF and Word2Vec, for text representation, and hybrid deep learning and machine learning algorithms for classification. A convolutional neural network and Bidirectional long short-term memory (CNN-BiLSTM) model and the machine learning XGBoost model were used to classify social posts as suicidal or non-suicidal using textual and LIWC-22-based features by conducting two experiments. To assess the models' performance, we used the standard metrics of accuracy, precision, recall, and F1-scores. A comparison of the test results showed that when using textual features, the CNN-BiLSTM model outperformed the XGBoost model, achieving 95% suicidal ideation detection accuracy, compared with the latter's 91.5% accuracy. Conversely, when using LIWC features, XGBoost showed better performance than CNN-BiLSTM.

The moderation effect of entrepreneurial resilience on the relationship between financial literacy and sustainable performance.

The study intends to examine the role of financial literacy in sustainable performance of SME's in Saudi Arabia, with the moderating effect of entrepreneurial resilience. The data for this study were gathered from 203 different SME's sector entrepreneurs in Saudi Arabia using a convenience sampling technique. The hypothesis was tested through Smart-PLS software 3.3.9 version and structural equation modeling technique was used to verify the hypothesis relationships. The findings show that financial literacy has a significant and positive impact on sustainable performance. Moreover, results indicate that entrepreneurial resilience has a significant and positive effect on sustainable performance. Furthermore, the findings show that entrepreneurial resilience moderates the relationship between financial literacy and sustainable performance in Saudi Arabia. Lastly, this research article addressed the discussion and practical implications of the study.

Investigating the Impact of Institutions on Small Business Creation Among Saudi Entrepreneurs.

Institutions significantly impact people's attitudes and behaviors, both favorably and negatively. The purpose of this article is to examine the influence of several institutions on the intentions and decisions of Saudi entrepreneurs to start a business. Accordingly, the study on which this article is based used cross-sectional data of 3,376 respondents obtained from the Global Entrepreneurship Monitor (GEM) in 2016. The findings demonstrated that insufficient business legislations and policies have a detrimental impact on the ability to start small businesses. Furthermore, it was discovered that the more media attention is given to successful entrepreneurs, the greater the likelihood of small businesses being established. Surprisingly, the typically high regard for successful entrepreneurs had no positive impact on the establishment of small businesses in Saudi Arabia. Moreover, there was no negative impact of fear of failure on the likelihood of starting small firms. Finally, the influence of control variables, such as age and gender, was also varied. Because the study was limited to the context of Saudi Arabia, future research could focus on expanding the analysis to other Gulf countries and including more institutions.

The Impact of Financial Literacy on Entrepreneurial Intention: The Mediating Role of Saving Behavior.

This study explored the impact of financial literacy (financial awareness) on potential entrepreneurs' intent in Saudi Arabia. It also examined saving behavior as a mediator in the relationship between financial literacy and entrepreneurial intention. The study's data were collected by an online questionnaire sent to a sample of 270 potential entrepreneurs at Abqaiq Applied College, affiliated with King Faisal University. Data analysis was done using partial least squares structural equation modeling (PLS-SEM). According to the findings, there is no direct relationship between financial literacy and entrepreneurial intent. However, it has been reported that saving behavior can mediate between financial literacy and entrepreneurial intent.

Exploring the Influence of Potential Entrepreneurs' Personality Traits on Small Venture Creation: The Case of Saudi Arabia.

This study examined the impact of selected personality traits-innovativeness, internal locus of control, need for achievement and propensity to take risks-on the entrepreneurial intention of Saudi students (young entrepreneurs). The study sample included 165 students from an applied college affiliated with King Faisal University. The participants completed an online self-administered questionnaire, the data from which were analyzed using the partial least squares structural equation modeling (PLS-SEM) method. The findings revealed that the characteristics of innovativeness, internal locus of control and propensity to take risks had a positive relationship with entrepreneurial intention. However, the need for achievement had no relationship with entrepreneurial intention. The study model predicted approximately 25% of the total variance in entrepreneurial intention. It is recommended that in future studies, the sample size should be increased and the scope of the study should be broadened.

Soft Clustering for Enhancing the Diagnosis of Chronic Diseases over Machine Learning Algorithms.

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.