Meri Badalyan

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.

Type of the article: Research Article

Tourism has become a key driver of inclusive growth and economic diversification in transition economies, including the Republic of Armenia, making it a priority area of development. This study evaluates the interaction between tourism sector indicators and national macroeconomic performance on GDP. Annual data for 2001–2023 were analyzed using a robust econometric framework that integrates LASSO regression, Granger causality tests, and Vector Autoregression (VAR) modeling. GDP was consistently treated as the dependent variable, while LASSO served only as a variable selection tool to identify the most influential indicators.

The results reveal that economic growth is shaped not only by quantitative expansion but also by qualitative improvements. Hotel infrastructure (β = 3.59, p = 0.011) and the share of tourism in total exports (β = 18.99, p = 0.046) significantly stimulate GDP, while international tourist arrivals show a negligible and slightly negative correlation (–0.000097, p = 0.036), underscoring the limited efficiency of visitor numbers without adequate monetization. Spatial analysis highlights a concentrated market structure, with 82% of revenues concentrated in Yerevan (HHI = 0.70), despite Kotayk’s high specialization index (LQ = 14.1). Regional elasticity estimates indicate that Vayots Dzor (ε ≈ 0.85), Kotayk (ε ≈ 0.67), and Syunik (ε ≈ 0.58) are most sensitive to tourism-driven growth, far above the national average (ε ≈ 0.34).

These findings suggest that Armenia’s tourism-led GDP growth depends on infrastructure modernization, high-value services, and balanced regional development. Policy interventions should prioritize decentralization and large-scale investment over simple visitor growth.