Bella Gabrielyan

Fiscal decentralization is a key component of effective economic governance, enhancing the role of local governments and promoting more efficient allocation of public resources. A central dimension of fiscal decentralization involves the distribution of tax revenues across different levels of government. The purpose of this study is to assess the level of fiscal autonomy of local governments in the Republic of Armenia using the OECD methodology and tax autonomy as a measure of local government taxing powers. By comparing Armenia with OECD unitary states through cluster analysis, the analysis identifies an optimal structure for local tax revenue systems. Armenia exhibits a low level of tax autonomy comparable to the weakest cluster of OECD unitary countries, which includes Estonia, Ireland, Israel, Lithuania, New Zealand, and the UK. The findings suggest that increasing local government revenues by deducting from existing indirect taxes, such as excise duty and VAT, is challenging due to administrative inefficiencies and difficulties in accurately estimating the tax base. Due to their narrow and centralized nature, excise taxes are poorly suited to local use, while the complexity of VAT and its allocation of revenue to customs authorities limits its effectiveness at the local level. In light of its findings, the study recommends shifting the focus toward direct taxes to improve local revenue more effectively and sustainably.

Type of the article: Research Article

Abstract
This study investigates the challenges and opportunities of artificial intelligence (AI) adoption in human resources management (HRM) within the ICT industry of Armenia. Utilizing a mixed-methods approach, the analysis is based on a structured survey and in-depth interviews conducted with 30 HR specialists from Armenian ICT companies in Yerevan between January and February 2025. The results of the expert survey were analyzed using descriptive statistics, frequency analysis, and cross-tabulation tests in SPSS software. The findings revealed significant opportunities for using AI in human resources management within the ICT sector. These opportunities include improving management processes for employees and saving time and financial resources through the effective use of artificial intelligence in HRM. However, there are also evident challenges, such as the comparatively slow rate of AI integration in HRM (only 43.4% use AI tools in HRM), and risks associated with human–AI imbalance (27.8%), information protection (27.8%), job displacement (18.5%), AI bias (16.7%), and resistance to change (9.3%). Nevertheless, the findings revealed no correlation between company size and the level of AI implementation in HRM (Pearson Chi-Square = 0.143, p = 0.931), which does not support the hypothesis of a ‘digital divide’ within the sector whereby larger companies are more likely to implement AI than small and medium-sized enterprises. The study highlights the importance of balancing AI technology with the human factor, developing ethical standards, investing in AI literacy, and implementing targeted training programs.