The youth entrepreneurship has been regarded as a tool for employment creation in Ethiopia. However, the country faces the challenge of high youth unemployment rates. Even the country is considered as one of the lowest rates of youth entrepreneurial activity and business operating in the sub-Saharan African region. The aim of this research is to investigate the major potential constraints of youth entrepreneurs in the small and micro-enterprises. In order to fulfill the objectives of the study, this research employed a descriptive research design with a quantitative research approach. The target population of the study was 5000 youth entrepreneurs between aged 18 and 29 years at the time of the research. Out of the target population, the researchers selected 370 as a sample size by using a proportional stratified sampling technique. The valid responses for analysis were 350 respondents. The survey questionnaire was used as a tool for data collection. The study used descriptive statistics and factor analysis. The results of the study indicate that unfavorable government policy, limited access to finance, limited access to market, limited access to information and infrastructure, limited access to business assistance and support, limited access to entrepreneurship training and education, lack of social support, unfavorable administrative, and weak institutional linkage were the major potential constraints of youth entrepreneurs in the study area. The study draws implications for policymakers, the Ministry of Youth Development, Chamber of Commerce and Industry, and the government to adopt multi-faceted, multi-targeted, and multitier approaches in order to facilitate and encourage youth entrepreneurship.

Humans have traditionally found it simple to identify emotions from facial expressions, but it is far more difficult for a computer system to do the same. The social signal processing subfield of emotion recognition from facial expression is used in a wide range of contexts, particularly for human-computer interaction. Automatic emotion recognition has been the subject of numerous studies, most of which use a machine learning methodology. The recognition of simple emotions like anger, happiness, contempt, fear, sadness, and surprise, however, continues to be a difficult topic in computer vision. Deep learning has recently drawn increased attention as a solution to a variety of practical issues, including emotion recognition. In this study, we improved the convolutional neural network technique to identify 7 fundamental emotions and evaluated several preprocessing techniques to demonstrate how they affected the CNN performance. This research focuses on improving facial features and expressions based on emotional recognition. By identifying or recognising facial expressions that elicit human responses, it is possible for computers to make more accurate predictions about a person's mental state and to provide more tailored responses. As a result, we examine how a deep learning technique that employs a convolutional neural network might improve the detection of emotions based on facial features (CNN). Multiple facial expressions are included in our dataset, which consists of about 32,298 photos for testing and training. The preprocessing system aids in removing noise from the input image, and the pretraining phase aids in revealing face detection after noise removal, including feature extraction. As a result, the existing paper generates the classification of multiple facial reactions like the seven emotions of the facial acting coding system (FACS) without using the optimization technique, but our proposed paper reveals the same seven emotions of the facial acting coding system.

The concept of work-life balance developed on the premise that career and personal life should not be seen as competing concerns but as balanced components of one’s overall existence. Initiatives to promote work-life balance become mandatory requirements for employees to maximize productivity and retain talent. This study examines the effect of work-life balance initiatives on organizational citizenship behavior in the case of commercial bank branches in Werabe (Ethiopia). Descriptive and explanatory research designs were employed by using a quantitative research approach. The data was collected through a standard questionnaire. The study used the census method. 150 employees from nine different commercial bank branches in Worabe town were selected to provide the study’s primary data. The collected data were analyzed using descriptive and inferential statistical tools. The study found that most employees’ perceptions were low for all significant work-life balance initiative variables included in this study; they were also medium in perceiving organizational citizenship behavior. Among the independent variables under investigation, three of them (flexible work arrangements, work leave programs, and dependent care initiatives) have positive and significant effects on organizational citizenship behavior. In contrast, the other two (working hours and workload) negatively and significantly affect organizational citizenship behavior. The beta coefficients of this study indicated that flexible work arrangements have a high effect on organizational citizenship behavior, followed by work leave programs. Therefore, it is better to adopt them in the organization. The influence of the abovementioned variables on organizational citizenship behavior is significant. This study fills this gap and extends the understanding of the role of variables. The bank’s management needs to offer initiatives by allowing employees to schedule their time and assisting and advising them on childcare costs and responsibilities such as schools, offering extended leave if something goes wrong, and sharing charges for education.

The successful implementation of effective data governance in universities is crucial in complementing the massive information technology (IT) developments in higher education institutions globally. Hence, to identify the enablers of IT governance, this study examined the effects of knowledge value, knowledge-oriented culture, and knowledge-sharing process on the integration of IT governance in the curriculum and the latter’s effects on academic performance and perceived employability among university students in Saudi Arabia. We used a cross-sectional approach to collect quantitative data from 280 university students and employed structural equation modelling and partial least squares to conduct the data analysis. Our results confirmed the significant positive effects of knowledge value and the knowledge-sharing process on IT governance, along with a positive significant effect of IT governance on academic performance and perceived employment. Moreover, the results revealed a direct, positive, and significant effect of academic performance on perceived employment, as well as the significant mediating effects of IT governance and academic performance between these two. Apart from extending the relevant literature, this study offers several policy implications.

Employee engagement refers to the commitment and willingness of employees to go beyond their defined tasks for ensuring the success of their organization. Prioritizing the development of engaged workers reaps substantial rewards for organizations in terms of productivity, efficiency, and effectiveness (Chiwawa, 2022). Employees’ job engagement has gained momentum in the past few decades, mainly due to the changing work dynamics, workforce diversity, and globalization (Awan, Aslam, & Mubin, 2019). The aim of this study is to identify the determinants of employee engagement in the emerging market among Silite zone teachers through a cross-sectional descriptive and explanatory research design. The total population of the study is 5,902, using Yamane’s formula 375 has been selected as a sample size with a stratified and simple random sampling technique. The collected data were analyzed by using correlation and regression models and a general linear model of univariate analysis of variance through SPSS version 26. The results of the study revealed that reward and recognition, work-life balance, training and development, and job characteristics are significant in determining employee engagement, whereas communication was found to be insignificant. The study recommends that Silite zone schools reconsider and continue the good work in reward and recognition practices, balancing employee work life, its ways of training and development for the teachers, and improving the job character.