This paper aims to analyze the impact of access to Information and Communication Technologies (ICT) on the private returns to higher education (HE) focusing on gender inequality in 2020. Methodology: To evaluate the above impact a set of Mincerian equations will be estimated. The proposed approach mitigates biases associated with self-selection and individual heterogeneity. Data: The database comes from the National Household Income and Expenditure Survey (Encuesta Nacional de Ingresos y Gastos de los Hogares, ENIGH) from 2020. Results: Empirical evidence suggests that individuals that have HE have a positive and greater impact on their salary income compared to those with a lower educational level, being women that do not have access to ICT those with the lowest wage return. Policy: Access to ICT should be considered as one of the criteria that integrate social deprivation in the measurement of multidimensional poverty. Likewise, it is necessary to design public policies that promote the strengthening and creation of educational and/or training systems in technological matters for women. Limitations: No distinction was made between individuals that graduated from public or private schools, nor was income from sources other than work considered. Originality: This investigation evaluates the impact of access to ICT on the returns to higher education in Mexico, in 2020, addressing gender disparity.

The fifth-generation technology standard (5G) is the cellular technology standard of this decade and its adoption leaves room for research and disclosure of new insights. 5G demands specific skillsets for the workforce to cope with its unprecedented use cases. The rapid progress of technology in various industries necessitates a constant effort from workers to acquire the latest skills demanded by the tech sector. The successful implementation of 5G hinges on the presence of competent individuals who can propel its progress. Most of the existing works related to 5G explore this technology from a multitude of applied and industrial viewpoints, but very few of them take a rigorous look at the 5G competencies associated with talent development. A competency model will help shape the required educational and training activities for preparing the 5G workforce, thereby improving workforce planning and performance in industrial settings. This study has opted to utilize the Fuzzy Delphi Method (FDM) to investigate and evaluate the perspectives of a group of experts, with the aim of proposing a 5G competency model. Based on the findings of this study, a model consisting of 46 elements under three categories is presented for utilization by any contingent of 5G. This competency model identifies, assesses, and introduces the necessary competencies, knowledge, and attributes for effective performance in a 5G-related job role in an industrial environment, guiding hiring, training, and development. Companies and academic institutions may utilize the suggested competency model in the real world to create job descriptions for 5G positions and to develop curriculum based on competencies. Such a model can be extended beyond the scope of 5G and lay the foundation of future wireless cellular network competency models, such as 6G competency models, by being refined and revised.

With the advent of the big data era, the amount of various types of data is growing exponentially. Technologies such as big data, cloud computing, and artificial intelligence have achieved unprecedented development speed, and countries, regions, and multiple fields have included big data technology in their key development strategies. Big data technology has been widely applied in various aspects of society and has achieved significant results. Using data to speak, analyze, manage, make decisions, and innovate has become the development direction of various fields in society. Taxation is the main form of China’s fiscal revenue, playing an important role in improving the national economic structure and regulating income distribution, and is the fundamental guarantee for promoting social development. Re examining the tax administration of tax authorities in the context of big data can achieve efficient and reasonable application of big data technology in tax administration, and better serve tax administration. Big data technology has the characteristics of scale, diversity, and speed. The effect of tax big data on tax collection and management is becoming increasingly prominent, gradually forming a new tax collection and management system driven by tax big data. The key research content of this article is how to organically combine big data technology with tax management, how to fully leverage the advantages of big data, and how to solve the problems of insufficient application of big data technology, lack of data security guarantee, and shortage of big data application talents in tax authorities when applying big data to tax management.

Mediating role of artificial intelligence in the relationship between higher education quality and scientific research ethics among faculty members: A Study in carrying out the study, specific research objectives were derived, and based on the derived objectives, null hypotheses were formulated and tested for the study. This study, thus, employed survey research design. This study’s population comprised postgraduate students from Middle Eastern University, Jordan, with 1200 students. Using the population, a sample size of 291 respondents was selected based on Krecie and Morgan The students in the sample completed Google Forms questionnaires. The data were statistically processed, and the analysis’s most significant level was 0.25. The research questions were analyzed using descriptive statistics, and the null hypothesis was tested using Pearson Product Moment Correlational Analysis (PPMC). Also, the study showed a significant relationship between artificial intelligence and the quality of higher education and the relationship of significance between artificial intelligence and ethics in scientific research. The researcher suggested a need for ongoing education, cross-discipline cooperation, and the development of solid ethical frameworks for the integration ethics of AI academia.

This study examines the impact of Human Resource Management (HRM) practices, specifically Compensation, Job Design, and Training, on employee outcomes, including Engagement, Efficiency, Customer Satisfaction, and Innovation within an organizational framework. Employing a quantitative research methodology, the study utilizes a cross-sectional survey design to collect data from employees within a public service organization, analyzing the relationships through structural equation modelling. Findings reveal significant positive relationships between HRM practices and employee performance metrics, highlighting the pivotal role of Employee Engagement as a mediator in enhancing organizational effectiveness. Specifically, Compensation and Job Design significantly influence Employee Engagement and Efficiency, while training is crucial for driving Innovation and Customer Satisfaction. The practical implications of this research underscore the necessity for organizations to adopt integrated and strategic HRM frameworks that foster employee engagement to drive performance outcomes. These insights are vital for HR practitioners and organizational leaders aiming to enhance workforce productivity and innovation. In conclusion, the study contributes valuable perspectives to the HRM literature, advocating for holistic HRM practices that optimize employee well-being and ensure organizational competitiveness. Future research is encouraged to explore these dynamics across various sectors and cultural contexts to validate the generalizability of the findings.

Purpose—In the business sector, reliable and timely data are crucial for business management to formulate a company’s strategy and enhance supply chain efficiency. The main goal of this study is to examine how strong brand strength affects shareholder value with a new Supplier Relationship Management System (SRMS) and to find the specific system qualities that are linked to SRMS adoption. This leads to higher brand strength and stronger shareholder value. Design/Methodology/Approach—This study employed a cross-sectional design with an explanatory survey as a deductive technique to form hypotheses. The primary method of data collection used a drop-off questionnaire that was self-administered to the UAE-based healthcare suppliers. Of the 787 questionnaires sent to the healthcare suppliers, 602 were usable, yielding a response rate of 76.5%. To analyze the data gathered, the study used Partial Least Squares Structural Equation modelling (PLS-SEM) and artificial neural network (ANN) techniques. Findings—The study’s data proved that SRMS adoption and brand strength positively affected and improved healthcare suppliers’ shareholder value. Additionally, it demonstrates that user satisfaction is the most significant predictor of SRMS adoption, while the results show that the mediating role of brand strength is the most significant predictor of shareholder value. The results demonstrated that internally derived constructs were better explained by the ANN technique than by the PLS-SEM approach. Originality/Value—This study demonstrates its practical value by offering decision-makers in the healthcare supplier industry a reference on what to avoid and what elements to take into account when creating plans and implementing strategies and policies.