The current study examines the impact that technological innovation, foreign direct investment, economic growth, and globalization have on tourism in top 10 most popular tourist destinations in the world. The information on the number of tourists, foreign direct investment, growth in gross domestic product, GFCF, use of FFE, and total energy consumption were extracted from the World Development Indicators. The United Nations Conference on Trade and Development (UNCTAD) database was used for collecting the statistics about technological innovation.  The source ETH Zurich has been utilized to gather panel data for the time period 2008 to 2022 to calculate the KOF Index of Globalization. Theoretically, FDI and Economic growth are the endogenous variables for the Tourism model. Whereas, TI, Glob, Energy Consumption, and GFCF are the exogenous variables. Hence, the analysis is based on the System Equation—Simultaneous equations, after checking identification that confirms the problem of simultaneity in system of 3 equations. The empirical outcomes suggest that TI, FDI, globalization index, GDP growth, and energy consumption are the most important factors that contribute to an increase in tourism. Likewise FDI as the endogenous variable is favorably impacted by globalization, technological innovation, fossil fuel energy consumption, gross fixed capital formation, and tourism. Nevertheless, the coefficient of GFCF is only insignificant in the study. While, globalization, TI, and FFE are also favorably affecting the FDI. GDP growth is the second endogenous variable in this research, and it is positively influenced by globalization, FDI, and tourism in the case of the top 10 nations that are most frequently visited by tourists.

The root of the problem in this research is the fact that scientific writing with a national reputation is still low and the publication of scientific writing with a national reputation is also low, thus affecting the quality of lecturers at the University. To overcome this problem, this research developed a training management model that can improve the scientific writing skills of lecturers and familiarize lecturers to actively conduct nationally reputable scientific writing. The training management model in question is called the “National Reputable Scientific Writing Training Management” model. This type of research is development research or R&D to produce a valid, practical, and effective model, as well as all devices and research instruments related to the application of the model at the University. The results showed that: (1) the National Reputable Scientific Writing Training Management model is suitable for improving the scientific writing ability of lecturers; (2) the output of the National Reputable Scientific Writing Training Management model in the model group is significantly higher than the initial group (pre-model); (3) The average value of IP/IO from experts is 4.4 with a high category, from observers at stage I test is 4.0 with a high category, at stage II test is 4.7 with a high category and stage III test is 4.77 with a high category, so it is concluded that the National Reputable Scientific Writing Training Management model meets the criteria of effectiveness, practicality and implementation; (4) The response of university managers and respondents to the implementation of the model is quite satisfactory, both regarding the concept of the model, the application in technical implementation and their perception of the National Reputable Scientific Writing Training Management model; and (5) the National Reputable Scientific Writing Training Management model can be developed as an alternative implementation in training management at the university.

The Government of Indonesia has modernized the toll road transaction system by implementing the multi-lane free-flow (MLFF) project, set to operate commercially by the end of 2024. This project leverages Global Navigation Satellite System (GNSS) technology to identify vehicles using toll roads and establish a transaction mechanism that allows the MLFF Project Company to charge road users according to distance, vehicle category, and tariff levels. The project has result in a complex business arrangement between the Indonesia National Toll Road Authority (INTRA), Toll Road Companies (TRCs), and the MLFF Project Company. The aim of this paper is to review the regulatory and institutional framework of the MLFF project and analyze its challenges. The methodology employed is a qualitative framework for legal research, utilizing international literature reviews and current regulatory frameworks. The study assesses the proposed transaction architecture of the project and identifies commercial, political, and other risks associated with its implementation. Based on the analysis, the research identifies opportunities for regulatory improvements and better contracting arrangements. This research provides valuable insights into the regulatory landscape and offers policy recommendations for the Government to mitigate the identified risks. This contribution is significant to the academic field as it enhances understanding regulatory and institutional challenges in implementing advanced toll road systems.

Projects implemented under life cycle contracts have become increasingly common in recent years to ensure the quality of construction and maintenance of energy infrastructure facilities. A key parameter for energy facility construction projects implemented under life cycle contracts is their duration and deadlines. Therefore, the systematic identification, monitoring, and comprehensive assessment of risks affecting the timing of work on the design and construction is an urgent practical task. The purpose of this work is to study the strength of the influence of various risks on the duration of a project implemented on the terms of a life cycle contract. The use of the expert assessment method allows for identifying the most likely risks for the design and construction phases, as well as determining the ranges of deviations from the baseline indicator. Using the obtained expert evaluations, a model reflecting the range and the most probable duration of the design and construction works under the influence of risk events was built by the Monte-Carlo statistical method. The results obtained allow monitoring and promptly detecting deviations in the actual duration of work from the basic deadlines set in the life cycle contract. This will give an opportunity to accurately respond to emerging risks and build a mutually beneficial relationship between the parties to life cycle contracts.

The maize commodity is of strategic significance to the South African economy as it is a stable commodity and therefore a key factor for food security. In recent times climate change has impacted on the productivity of this commodity and this has impacted trade negatively. This paper explores the intricate relationship between climatic factors and trade performance for the South African maize. Secondary annual time series data spanning 2001 to 2023, was sourced from an abstract from Department of Agriculture, Land Reform and Rural Development (DALRRD) and World Bank’s Climate Change Knowledge Portal. Autoregressive Distributed Lag (ARDL) cointegration technique was used as an empirical model to assess the long-term and short-term relationships between explanatory variables and the dependent variable. Results of the ARDL model show that, average annual rainfall (β = 2.184, p = 0.056), fertilizer consumption (β = 1.919, p = 0.036), gross value of production (β = 1.279 , p = 0.006) and average annual surface temperature (β = −0.650, p = 0.991) and change in temperature for previous years, (β = −0.650, p = 0.991) and the effects towards coefficient change for export volumes, (β = 0.669, p = 0.0007). In overall, as a recommendation, South African policymakers should consider these findings when developing strategies to mitigate the impacts of some of these climatic factors and implementing adaptive strategies for maize producers.

As the aging trend intensifies, the Chinese government prioritizes technological innovation in smart elderly care services to enhance quality and efficiency, catering to the diverse needs of the elderly. This study examines the acceptance and usage behavior of smart elderly care services among elderly individuals in Xi’an, using a modified Unified Theory of Acceptance and Use of Technology (UTAUT) model that includes digital literacy as a moderating variable. Data were collected via a survey of 299 elderly individuals aged 60 and above in Xi’an. The study aims to identify factors influencing the acceptance and usage behavior of smart elderly care services and to understand how digital literacy moderates the relationship between these factors and usage behavior. Regression analysis assessed the direct effects of Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) on usage behavior. These dimensions were then integrated into a comprehensive index Service Acceptance to evaluate their overall impact on usage behavior, with behavioral intention examined as a potential mediating variable. Results indicate that EE and SI significantly impact the adoption of smart elderly care services, whereas PE and FC do not. Behavioral intention mediates the relationship between these variables and usage behavior. Additionally, gender, age, and digital literacy significantly moderate the impact of service acceptance on usage behavior. This study provides valuable theoretical and practical insights for designing and promoting smart elderly care services, emphasizing the importance of usability and social promotion to enhance the quality of life for the elderly.