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.

COVID-19 has presented considerable challenges to fiscal budget allocations in developing countries, significantly affecting decisions regarding number of investments in the transport sector where precise resource allocation is required. Elucidating the long-term relationship between public transport investment and economic growth might enable policymaker to effectively make a decision in regard to those budget allocation. Our paper then utilizes Thailand as a case study to analyze the effects on economic growth in a developing country context. The study employs Cointegration and Vector Error Correction Model (VECM) techniques to account for long-term correlations among explanatory variables during 1991–2019. The statistical findings reveal a significantly positive correlation between transport investment and economic growth by indicating an increase of 0.937 in economic growth for every one-percent increment in transport investment (S.D. = 0.024, p < 0.05). This emphasizes the potential of expanding the transport investment to recover Thailand’s economy. Furthermore, in terms of short-term adjustments, our results indicate that transport investment can significantly mitigate the negative impact of external shocks by 0.98 percent (p < 0.05). These findings assist policymakers in better managing national budget allocations in the post-Covid-19 period, allowing them to estimate the duration of crowding-out effects induced by shocks more effectively.

New technologies always have an impact on traditional theories. Finance theories are no exception to that. In this paper, we have concentrated on the traditional investment theories in finance. The study examined five investment theories, their assumptions, and their limitation from different works of literature. The study considered Artificial Intelligence (AI) and Machine Learning (ML) as representative of financial technology (fintech) and tried to find out from the literature how these new technologies help to reduce the limitations of traditional theories. We have found that fintech does not have an equal impact on every conventional finance theory. Fintech outperforms all five traditional theories but on a different scale.

The global economic recession has caused pessimism in terms of prospects of sales recovering in the future. The present study is an attempt to investigate the cost stickiness behavior by focusing on specific characteristics of companies. The research was done through documentary analysis and access to quantitative data, with the use of statistical methods for analysis as panel data. The statistical population of the actual study included all companies listed on the India stock exchange from 2017 to 2021. They were selected after screening 128 listed companies. The regression method was used to examine the relationship between variables and to present a forecast model. The results of testing the first hypothesis showed that companies’ costs are sticky and according to the results of this hypothesis, an increase in costs when the level of activity increases is greater than the level of reduction in costs when the volumes of the activities are decreased. The results of the second hypothesis showed a remarkable relationship between the cost stickiness and specific characteristics of companies (size, number of employees, long-term assets, financial leverage, and accuracy of profits forecast). Based on the third hypothesis, there is a notable difference between cost stickiness at different levels of specific characteristics of companies. Therefore, the results show that environmental uncertainty such as COVID-19, increases cost stickiness.

The global significance of the energy crisis and the need for a sustainable European electricity system have intensified interest in renewable energy sources. This study aims to explore the attitudes toward solar energy systems among the population of the North Transdanubian region, which is crucial for companies in the region specializing in solar system installation. The research sheds light on trends in energy prices, potential strategies for addressing the energy crisis, and the regulatory environment for solar systems in Hungary and Austria, focusing on the Burgenland region. The study is divided into two main sections: secondary and primary research. The secondary research presents various applications of renewable energy sources, especially solar energy, and examines energy pricing trends in the two countries, with particular emphasis on the payback period and the impact of changes in energy prices. The primary research is also divided into two parts: the first examines the satisfaction of customers who already use solar systems, and the second focuses on the attitudes of potential customers toward solar investments. The findings provide a comprehensive view of both current users’ and prospective investors’ perspectives on solar energy systems. The practical significance of this research lies in identifying development opportunities for companies, advancing energy efficiency goals, and supporting sustainability efforts.