Yazar "Seker, Ferhat" seçeneğine göre listele

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    Combining the power of artificial intelligence and mathematical modelling: A hybrid technique for enhanced forecast of tourism receipts
    (Varna Univ Management-Vum, 2024) Seker, Ferhat
    Despite being one of the most visited countries in the world, Turkiye's share of tourism revenue does not rank among the top ten. Therefore, it would be worth researching tourist expenditures and analysing this data could provide valuable insights. This research develops a novel approach to estimating and modelling tourism receipts by analysing expenditure types. Artificial intelligence-based methods, such as machine learning, have been increasingly used in the tourism literature to improve various aspects of the industry. However, little research has been conducted using a hybrid method to model and estimate tourist expenditure. This paper is the first to combine conventional mathematical analysis, specifically first-order two-variable polynomial equations, with artificial intelligence-based machine learning algorithms in a tourism setting. The research results indicate that expenditure types such as accommodation and food & beverage significantly impact Turkiye's tourism revenue and Turkiye's total tourism revenue will not exceed 45 billion dollars by 2027. This study provides a valuable and practical contribution to improving the accuracy and efficiency of methods for managing tourism economics, particularly in European countries where the economy heavily relies on income generated by tourism. Additionally, it fills a gap in studies focused on tourists' expenditure types by combining artificial intelligence and traditional analysis, making it a unique piece of research.
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    Harmonizing Heritage and Artificial Neural Networks: The Role of Sustainable Tourism in UNESCO World Heritage Sites
    (Mdpi, 2023) Bozkurt, Alper; Seker, Ferhat
    The classification of the United Nations Educational, Scientific, and Cultural Organization (UNESCO) World Heritage Sites (WHS) is essential for promoting sustainable tourism and ensuring the long-term conservation of cultural and natural heritage sites. Therefore, two commonly used techniques for classification problems, multilayer perceptron (MLP) and radial basis function (RBF) neural networks, were utilized to define the pros and cons of their applications. Then, according to the findings, both correlation attribute evaluator (CAE) and relief attribute evaluator (RAE) identified the region and date of inscription as the most prominent features in the classification of UNESCO WHS. As a result, a trade-off condition arises when classifying a large dataset for sustainable tourism between MLP and RBF regarding evaluation time and accuracy. MLP achieves a slightly higher accuracy rate with higher processing time, while RBF achieves a slightly lower accuracy rate but with much faster evaluation time.
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    Öğe
    The factors affecting tourism mobile apps usage
    (Escola Superior Gestao, Hotelaria & Turismo Univ Algarve, 2023) Seker, Ferhat; Kadirhan, Gokhan; Erdem, Ahmet
    The purpose of this study is to determine the key factors affecting the behaviour of using tourism mobile apps. Contrary to previous studies, the present paper highlights the key factors by evaluating the perceived advantages and technological self-efficacy together. So as to evaluate overall measurement quality and test the hypothesised relationships, a two-step approach was applied. In the first step, confirmatory factor analysis (CFA) was employed to test the validity of the measurement scales. Then, the dataset was analysed using the PLS-SEM method to test the proposed hypotheses. Data were collected from 213 adult participants through an online survey. The study revealed that time-saving is a key determinant of tourism mobile apps usage with the highest beta coefficient (0.335, p<0.01). The effects of convenience (0.293) and technological self-efficacy (0.201) were also significant and positive. However, the perceived financial advantage does not have a significant effect on the behaviour of tourism mobile apps usage. Given the growing value and market potential of mobile applications, this research provides crucial empirical evidence for application developers and tourism researchers about the use of mobile applications for the tourism industry.