Мақола

AI-Driven Remarketing and Digital Infrastructure in Emerging Markets: Evidence from Tourism and Textile Enterprises in Uzbekistan

Silvia BeloevaDepartment of Management and Social Activities, Faculty of Business and Management, “Angel Kanchev” University of Ruse, 8 Studentska Str., 7017 Ruse, BulgariaIzzatilla LevakovDepartment of Management, University of Business and Science, Namangan 160100, UzbekistanNataliya VenelinovaDepartment of Management and Social Activities, Faculty of Business and Management, “Angel Kanchev” University of Ruse, 8 Studentska Str., 7017 Ruse, BulgariaAzam AkhmedovDepartment of Management, University of Business and Science, Namangan 160100, UzbekistanMukhtorjon MakhmudovDepartment of Management, University of Business and Science, Namangan 160100, Uzbekistan

Sustainabilityjournal2026en

ABI

Аннотация

This study comparatively evaluates the effectiveness of remarketing strategies under digital transformation in Uzbekistan’s service (tourism and hospitality) and manufacturing (textile) sectors, grounded in the Resource-Based View (RBV) and the Technology Acceptance Model (TAM). Using a sequential explanatory mixed-methods design, 280 enterprises (140 per sector) from four regions of Uzbekistan were surveyed, integrating quantitative analysis (OLS regression, t-test, χ2-test, PLS-SEM) and Monte Carlo simulation (20,000 iterations) with qualitative in-depth interviews (n = 32). The textile sector exhibited higher but more volatile returns (ROI = 82.1%; CV = 0.18), whereas the tourism sector achieved more stable yet lower returns (ROI = 48.3%; CV = 0.11) (t(278) = −22.84; p < 0.001; Cohen’s d = 2.73). AI-based personalization was positively associated with ROI (β = 0.28, p < 0.001) and with reduced revenue volatility through an indirect pathway (indirect effect = 5.04, 95% CI [4.10, 6.00]), with significantly stronger associations in the textile sector (Δ = 1.64, p < 0.05). This study contributes to digital marketing theory by demonstrating sector-specific heterogeneity in AI personalization mechanisms, providing empirical evidence of the infrastructure–ROI variability relationship in a transition economy, and demonstrating the value of integrating Monte Carlo–based uncertainty analysis with mixed-methods evidence as a robustness device. The findings carry direct implications for sustainable economic development in transition economies: by demonstrating how sector-specific digital marketing strategies are linked to and can enhance the long-term viability and resource efficiency of enterprises, this study contributes to advancing Sustainable Development Goal 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production).

Ҳали таржима қилинмаган

Мавзулар

Digital Transformation in Industry E-commerce and Technology Innovations Cyberloafing and Workplace Behavior

Идентификаторлар

DOI: 10.3390/su18115739

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