The endogenous, human, and social factors influencing the economic development of the municipalities of San Juan Cotzocón and San Pedro y San Pablo Ayutla in the Istmo de Tehuantepec region of the state of Oaxaca are analyzed. The hypothesis posits that the dimensions of endogenous development, social capital, and human capital directly impact the economic development of the respective municipalities. The study involved administering 262 questionnaires to the residents of these municipalities during the month of May 2023. The collected data were examined using exploratory factor analysis to determine the underlying structure and structural equation modeling to estimate the effects and relationships between variables. Results indicate that endogenous development, social capital, and human capital are factors in the economic development of the studied communities, with endogenous development being the most influential factor due to its statistical significance. Notably, the existence of tourist and cultural attractions in the municipalities emerges as a catalyst for local economic development in response to the establishment and operation of the Isthmus of Tehuantepec Interoceanic Corridor.

This article analyses the case of Dubai’s smart city from a public policy perspective and demonstrates how critical it is to rely on the use of the public-private partnership (PPP) model. Effective use of this model can guarantee the building of a smart city that could potentially fulfill the vision of the political leadership in Dubai and serve as a catalyst and blueprint for other Gulf states that wish to follow Dubai’s example. This article argues that Dubai’s smart city project enjoys significant political support and has ambitious plans for sustainable growth, and that the government has invested heavily in developing the necessary institutional, legal/regulatory, and supervisory frameworks that are essential foundations for the success of any PPP project. The article also points to some important insights that the Dubai government can learn from the international experience with the delivery of smart cities through PPPs.

Human resources are considered an important resource for companies today because the knowledge that a person has can be used to become an organisation’s competitive advantage. In addition, digital marketing has an important role in determining the performance of business entities because we have now entered the digital era, which certainly cannot be separated from the influence of technology on marketing through social media. Therefore, this study aims to examine the effect of Strategic Human Resource Management (SHRM) in digital marketing on business entity performance, which is determined by digital marketing in a business entity. The data in this research was collected by distributing questionnaires to 455 Micro Small Medium Enterprises (MSMEs) in Indonesia. Data analysis used the Moderated Regression Analysis (MRA) method. The research results show that strategic human resource management variables influence business performance, and the support of digital marketing capabilities and activities strengthens this influence. Based on the results of this research, existing business entities must strengthen organizational performance by strengthening human resources in basic soft skills and hard skills and skills in digital marketing and improving marketing activities using digitalization.

This paper provides new evidence on human resources management within the public sector. We explore the impact and mechanisms of the education and skills of tax inspectors on tax uncertainty using data from A-share-listed companies from 2009 to 2016. Our findings show that tax uncertainty is negatively correlated with the increase in human capital in the tax inspection bureau. That is, tax inspectors with higher levels of education and those who are certified tax agents help reduce tax uncertainty. Further analysis demonstrates that the impact of tax inspectors on tax uncertainty is most pronounced within large-scale and long-established firms.

The cost of diagnostic errors has been high in the developed world economics according to a number of recent studies and continues to rise. Up till now, a common process of performing image diagnostics for a growing number of conditions has been examination by a single human specialist (i.e., single-channel recognition and classification decision system). Such a system has natural limitations of unmitigated error that can be detected only much later in the treatment cycle, as well as resource intensity and poor ability to scale to the rising demand. At the same time Machine Intelligence (ML, AI) systems, specifically those including deep neural network and large visual domain models have made significant progress in the field of general image recognition, in many instances achieving the level of an average human and in a growing number of cases, a human specialist in the effectiveness of image recognition tasks. The objectives of the AI in Medicine (AIM) program were set to leverage the opportunities and advantages of the rapidly evolving Artificial Intelligence technology to achieve real and measurable gains in public healthcare, in quality, access, public confidence and cost efficiency. The proposal for a collaborative AI-human image diagnostics system falls directly into the scope of this program.