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Enhancing Big Data Solutions in the Tourism Industry Economic Efficiency and Forecasts Indicators

Year: 2026

Type: article

Abstract: The article examines scientific and methodological approaches to assessing and modeling the efficiency of the tourism industry based on Big Data. It substantiates mechanisms for quantitatively evaluating the impact of Big Data investments on profitability, integrating direct and indirect efficiency indicators, hybrid forecasting of tourism demand, DEA-based destination efficiency analysis, optimal resource allocation, and early risk detection. The KSBI index, KMTM resource allocation model, hybrid ARIMA + ML forecasting model, and IXK risk indicator are proposed as a methodological basis for managing the tourism industry in the digital economy.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.22688399

Author: Temirov, E.I.

Institution: Tashkent State University of Economics

Language: English

Topic: Diverse Aspects of Tourism Research

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Decent work and economic growth

Open Access status: green

OpenAlex ID: W7212126556

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