Search

The article examines the activity of the municipality from the point of view of the education management system in modern realities, namely in the conditions of digital transformation of society and its life, in particular, digitalization of educational activities. The main advantages and disadvantages of the digitalization of education are analyzed and presented. In the context of the research, the theoretical, normative and legal aspects of the current educational activity are presented, as a key link in solving problems of any nature for the benefit of the local population, in particular at the municipal level. In conclusion, the priority directions of the development of education management in the conditions of digitalization are given, which should effectively influence the optimization of organizational and management processes in the field of digital transformation, as well as the active penetration of digital technologies into the educational process of various educational organizations. The article reveals the effectiveness of information and communication technologies in municipal educational institutions training future specialists. In the context of the implementation of the research goal, the role of digitization as a factor in the implementation of educational activities in the process of training specialists in the economic field is studied. In the modern conditions of the development of the information society, there is a need for fast and high-quality calculation of economic indicators. It is this need that prompts the widespread use of software accounting methods. Digitalization has absorbed various fields of human activity. [ABSTRACT FROM AUTHOR]

This study explores the impact of supply chain finance on industrial efficiency and the internal mechanism. The super-efficiency model and quantitative analysis method were used based on the time series data of the ICT industry in Shenzhen from 2005 to 2020. In order to better investigate the impact of supply chain finance on industrial efficiency, this study categorized industrial efficiency into operational efficiency and innovation efficiency to reflect the durability and virtuousness of the industry development. Results indicate that supply chain finance has an inverted U-shaped nonlinear effect on industrial efficiency. Even though the long-term effect on operational efficiency is positive, the effect on innovation efficiency reveals an inverted U-shape, mainly due to the mediating effect of industrial concentration. Theoretically, this study sheds light on the intricate effects of supply chain finance at the industrial level, and also provides valuable insights into the potential inhibition mechanism, which enriches and deepens the related research on the supply chain finance from the nonlinear perspective, make the research findings more consistent with the fact of industrial development and avoid the linear stereotypes of the existing theory consensus. Practically, it is conductive to an objective understanding for managers and policymakers, so that supply chain finance can facilitate both operational and innovation efficiencies and realize the effective integration of supply chain finance and industrial development. Plain Language Summary: The purpose of this study aimed to explore the impact of supply chain finance on industrial efficiency. Authors conducted quantitative analysis based on the time series data of the ICT industry in Shenzhen from 2005 to 2020. In order to better investigate the impact of supply chain finance on industrial efficiency, this study categorized industrial efficiency into operational efficiency and innovation efficiency to reflect the durability and virtuousness of the industry development. Results indicate that supply chain finance can indeed greatly improve industrial efficiency. However, this improvement is more from the perspective of operational efficiency at the industrial operation level. In the long run, excessively developed supply chain finance will lead to excessive concentration on the supply side, and excessively powerful supply chain finance enterprises will also squeeze the survival space of small and medium-sized enterprises in the serviced industry, ultimately causing the entire industry to lose its innovative vitality due to the increased industrial concentration. Theoretically, this study sheds light on the intricate effects of supply chain finance at the industrial level, and also provides valuable insights into the potential inhibition mechanism, avoid the linear stereotypes of the existing theory consensus. Practically, it is conductive to an objective understanding for managers and policymakers to realize the effective integration of supply chain finance and industrial development. This study also encountered limitations from sample range and particularity, which make it faced the challenge of theoretical robustness when extending to traditional industries. [ABSTRACT FROM AUTHOR]

The purpose of the research is to test the effect of intellectual capital efficiency on firm profitability and firm value of firms operating in the information and communication technologies sector in Borsa Istanbul (BIST). The research sample consists of thirteen firms and covers the years between 2010 and 2020. The relationship between the effectiveness of intellectual capital components and firm performance is tested by constructing two different models and panel regression analysis. The dependent variables in the models are ROA (Return on Assets) and MTB (Market Value / Book Value) and the independent variables are the efficiency coefficients of the components in the VAICTM (Value Added Intellectual Capital Coefficient) model (HCE - human capital efficiency, SCE - structural capital efficiency, CEE - working capital efficiency). According to the research findings, the impact of intellectual capital components on firm performance is quite limited. Among the intellectual capital efficiency coefficients, only CEE has a positive effect on ROA. There is no relationship between intellectual capital efficiency components and MTB. [ABSTRACT FROM AUTHOR]

The information and communication technologies of the 21st century use digital user data as a technical tool and aim to increase the efficiency and profitability of industrial production through the use of advanced data analysis technologies. Big data technologies carry out continuous surveillance on internet users and can reach accurate inferences through advanced analysis methods by using data generated by users. The ability of big data analysis techniques to collect and process detailed behavioral data about users raises concerns regarding the privacy and confidentiality of user data. This study, which provides a summary of the developmental stages of data technologies and analyses the dangers contained in these technologies in connection with the ongoing and new dynamics of capitalism, includes a survey of 307 people. The survey consists of questions that measure participants' levels of caution and anxiety regarding their personal and behavioral data, internet usage habits, digital skills, and awareness levels of data privacy breaches. The sample included students selected from the Departments of Sociology, Philosophy and Computer Engineering at Hacettepe University and technology company employees at Hacettepe Teknokent to include three different levels of technological expertise. The study found that participants were also more likely to be concerned about their behavioral and sensitive data as their frequency of daily internet use increased. Users' attitudes to behavioral data vary according to their frequency of daily internet use and the level of expertise of the participants. Participants demonstrated a high level of awareness of online profiling, but the habit of keeping data regarding their online behavior private is not widespread. When the findings are evaluated broadly, it is noted that participants with a limited knowledge of the profiling capacity of data technologies are unable to maintain behavioral data confidentiality. This research has limitations in the context of the sample's capacity to represent the research universe. However, it is thought that it will prove useful in drawing the historical background for the development of big data technologies, analysing this technology within the dynamics of capitalism and in drawing attention to the differences between users' approach to personal data and behavioral data. [ABSTRACT FROM AUTHOR]

• This paper showcases the potential of information and communication technologies in traditional agriculture, as well as the issues to be encountered when they are applied to farming practices. • The challenges of robotics, IoT devices, and machine learning, as well as the roles of machine learning, artificial intelligence, and sensors used in agriculture, are all described in detail. In addition, drones are under consideration for conducting crop surveillance as well as for managing crop yield optimisation. • Additionally, whenever appropriate, global and state-of-the-art IoT-based farming systems and platforms are mentioned. We perform a detailed study of the recent literature in each field of our work. • From this extensive review, we conclude that the current and future trends of artificial intelligence (AI) and identify current and upcoming research challenges on AI in agriculture. The digitalization of data has resulted in a data tsunami in practically every industry of data-driven enterprise. Furthermore, man-to-machine (M2M) digital data handling has dramatically amplified the information wave. There has been a significant development in digital agriculture management applications, which has impacted information and communication technology (ICT) to deliver benefits for both farmers and consumers, as well as pushed technological solutions into rural settings. This paper highlights the potential of ICT technologies in traditional agriculture, as well as the challenges that may arise when they are used in farming techniques. Robotics, Internet of things (IoT) devices, and machine learning issues, as well as the functions of machine learning, artificial intelligence, and sensors in agriculture, are all detailed. In addition, drones are being considered for crop observation as well as crop yield optimization management. When applicable, worldwide and cutting-edge IoT-based farming systems and platforms are also highlighted. We do a thorough review of the most recent literature in each area of expertise. We conclude the present and future trends in artificial intelligence (AI) and highlight existing and emerging research problems in AI in agriculture due to this comprehensive assessment. [ABSTRACT FROM AUTHOR]