The tourism sector holds a significant position within national economic structures in terms of its capacity to generate economic growth, employment, regional development, and international revenue. Turkey, due to its rich natural, cultural, historical, and socioeconomic resources, stands out as a major tourism destination where diverse forms of tourism can develop simultaneously. However, tourism demand is not homogeneous in structure. Various tourism types—such as coastal, cultural, health, thermal, winter, faith, gastronomy, sports, and congress tourism—can be influenced by distinct regional, temporal, and economic dynamics.

Given the nature and payback period of tourism investments, basing investment decisions solely on current tourism intensity or historical trends may lead to overlooking future shifts in demand. In this context, the main objective of this study is to present a conceptual approach proposing that the future states of different tourism types and destinations can be modeled via stochastic processes—specifically Markov chains—based on their historical behaviors, and that the resulting probabilistic forecasts can be utilized to guide tourism investments.

The study first addresses the relationship between tourism demand, tourism diversity, and tourism investments, and subsequently explains the core properties of stochastic processes alongside the potential applications of Markov chains in tourism demand forecasting. In the proposed model, specific states are defined for tourism regions and tourism types to calculate transition probabilities between states using historical data, converting these probabilities into decision-support information regarding future tourism trends. In the subsequent stage of the study, the model is planned to be empirically tested using official tourism statistics of Turkey. Ultimately, the study aims to develop a data-driven decision-support framework for tourism investments that accounts for probabilistic future scenarios rather than relying exclusively on current status indicators.

Keywords: Tourism investments, stochastic processes, Markov chain, demand forecasting, decision support systems, Turkish tourism.