Heterogeneous associations between organizational circularity and enterprise economic performance in Ukraine and the EU

The increasing role of digitalization in the banking sector necessitates an in-depth analysis of the impact of information technology (IT) investments on bank profitability. The paper analyzes the influence of IT investments on the profitability of Vietnamese commercial banks. The data were collected from 27 commercial banks in Vietnam between 2010 and 2022. The methodology used in this paper is the Feasible Generalised Least Squares (FGLS) regression. The key results indicate that investment in IT has improved the overall performance of banks, as evidenced by an average increase of 1.8% in Return on Assets (ROA) and 15.3% in Return on Equity (ROE). In addition, the Equity-to-Asset ratio exerts a favorable influence on bank performance, increasing ROA by 15.7% and ROE by 40.9%. Furthermore, bank size also demonstrates a positive correlation with both ROA and ROE, raising it by 0.3% and 2.3%, respectively. Based on these findings, more efficient investment in digital transformation, collaboration with Fintech firms, IT competence enhancement for staff, and communication promotion for Vietnamese commercial banks are recommended. Enabling environments for bank digital transformation should be provided by the Government in building a centralized database and electronic systems, introducing fintech regulations, establishing digital ecosystems, and implementing security solutions.

Acknowlegment
This paper is funded by the National Economics University, Hanoi, Vietnam.
The authors would like to express their gratitude to the comments from chairs, scholars, and audiences at the 19th International Conference on Humanities & Social Sciences 2024 – Applying Humanities & Social Sciences for a sustainable future, Khonkhaen University, Thailand (ICHUSO-011). This paper has been revised significantly after presenting at the IC-HUSO 2024 Conference.

The development and implementation of regional tourism development programs in Ukraine require the formation of the information environment for ranking the tourist systems of Ukraine’s regions according to the attractiveness of their investment potential, which determines the purpose of this study. The methods of economic analysis and rating modeling were used to rank regional tourism systems according to their investment potential by determining the integral indicator of the economic and tourist resources of each region. Accordingly, economic indicators are chosen to provide the regions with financial, labor, and production resources; tourism indicators include supply and demand for tourism services, availability of tourism resources, tourism efficiency, as well as tourism accommodation. According to the results of rating and grouping, three groups of regions were formed: the first (the priority in terms of investment potential of the tourist system has the value of the integrated indicator from 72.0 to 94.8); the second (values from 40.2 to 71.0, reflecting the average level of investment potential); and the third (values from 25.0 to 40.1 for regions with low investment potential). The rating of regional tourist systems reflects the development level of their potential; it is an indicator of the effectiveness of its management and information base necessary for management decisions of potential investors. The asymmetry of investment potential of regional tourism systems causes an imbalance in their competitiveness, so implementing regional and national programs will help intensifying their use and equalizing regions’ development.

Acknowledgment
The paper reflects the research results within the study carried out in 2021–2024 at the State University of Trade and Economics at the request of the Ministry of Education and Science of Ukraine “Digital Transformation of Trade, Economic and Tourist Systems of Ukraine,” state registration number 0117U000503, and “Forecasting the impact of the tourist system on the country’s economy,” state registration number 0122U001559.

Many investors in order to predict stock prices use various techniques like fundamental analysis and technical analysis and sometimes rely on the discussions provided by various stock market analysts. ARIMA is a part of time-series analysis under prediction algorithms, and this paper attempts to predict the share prices of selected pharmaceutical companies in India, listed under NIFTY100, using the ARIMA model. A sample size of 782 time-series observations from January 1, 2017 to December 31, 2019 for each selected pharmaceutical firm has been considered to frame the ARIMA model. ADF test is used to verify whether the data are stationary or not. For ARIMA model estimation, significant spikes in the correlogram of ACF and PACF have been observed, and many models have been framed taking different AR and MA terms for each selected company. After that, 5 best models have been selected, and necessary inculcation of various AR and MA terms has been made to adjust the models and choose the best adjusted ARIMA model for each firm based on Volatility, adjusted R-squared, and Akaike Information Criterion. The results could be used to analyze the stock prices and their prediction in-depth in future research efforts.