Forecasting the success of a new tourism service by a neuro-fuzzy technique

Atsalakis, George S.; Atsalaki, Ioanna G.; Zopounidis, Constantin

doi:10.1016/j.ejor.2018.01.044

Cited by 36 publications

(27 citation statements)

References 35 publications

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“…It is a different perspective to make inferences with only tourists' past travel experiences. The overall accuracy rate obtained in studies in the literature is around 50-60%, which is consistent with this study [59], [60].…”

Section: Discussionsupporting

confidence: 92%

Hybrid Artificial Intelligence Based Automatic Determination of Travel Preferences of Chinese Tourists

Topal¹,

Uçar

2019

IEEE Access

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Background and Objective: Tourism and travel sector continues to grow by gaining an important place in the world economy and many countries want to increase their share in this sector. At the same time, it is known that today's consumer tourism and travel purchase decisions are influenced by social media. By examining the data of consumers on social media, it is possible for businesses to reach the right person and get more efficiency from high-cost promotion activities. The study aims to analyze the historical data of users on TripAdvisor with artificial intelligence methods to reveal a profile of consumers who might prefer Turkey. Methods: In this context, TripAdvisor, which is one of the best-known websites in the tourism sector, is an important source of data for countries to increase their share in the tourism market. Inferences can be made by using artificial intelligence methods and the data in TripAdvisor together. In this study, as a case study, the potentials of Chinese tourists to prefer Turkey are dealt because Turkey has increased its tourism targets ten folds for China and the year 2018 was declared as ''Turkey Tourism Year'' in China. In this context, this study aims to determine the potentials of Chinese tourists to prefer Turkey, by processing travel data histories obtained from TripAdvisor with artificial intelligence methods. It is expected that the study will contribute to the tourism sector as well as the academic literature. The study used the travel data history of Chinese tourists taken from TripAdvisor. Significant travel histories were selected by the F-score method. Depending on the selected and all travel histories of users, their travel preferences (Turkey/France) were classified by artificial intelligence algorithms. The developed model was tested with performance criteria. Results: At the end of the study, it was ensured that the Chinese, who would prefer Turkey, were determined with an accuracy rate of 75.25% and sensitivity rate of 0.76. Conclusions: It was observed that it is possible to find the tourists who will prefer Turkey by using the developed system. In other words, the study revealed that the countries can reach the individual instead of masses in their promotional activities.

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Section: Discussionsupporting

confidence: 92%

Hybrid Artificial Intelligence Based Automatic Determination of Travel Preferences of Chinese Tourists

Topal¹,

Uçar

2019

IEEE Access

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“…As a result, the training and testing correlation values are over 0.60 which is considered to be an acceptable value in the social sciences. At the same time, the correlation value is similar to the ANN studies performed in the field of business (King, Abrahams and Ragsdale, 2014;Atsalakis, Atsalaki and Zopounidis, 2018). However, the error rate is approximately 10%.…”

Section: E-wom Communication Motivational Factors Have Been Researchesupporting

confidence: 79%

Estimation With Artificial Neural Network on Electronic Word of Mouth: Opinion Searching

Topal

2020

Yönetim Bilimleri Dergisi

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Consumers make electronic word-of-mouth (e-WOM) on social media, which they trust as a source of information, by talking about products and services. In social media, e-WOM is used in three different ways: "opinion searching" (being the most common), "opinion giving", and "opinion forwarding". In this study, it is aimed to estimate the factors motivating "opinion searching" by artificial neural networks. Data was consisted of 512 questionnaires. Motivational factors were analysis by artificial neural network and Bayesian Regulation method. Results showed that the performance values were acceptable with 90% success rate. Identifying factors that motivate consumers for opinion searching can make a significant contribution to the achievement of marketing objectives of corporations. For this reason, e-WOM has been discussed in the recent literature with various motivational factors and analysis methods. This study differs from other research by combining motivational factors and detailing e-WOM behavior as well as using artificial neural networks. ELEKTRONİK AĞIZDAN AĞIZA İLETİŞİMDE YAPAY SİNİR AĞLARI İLE TAHMİNLEME: GÖRÜŞ ARAMAÖz Tüketiciler bir bilgi kaynağı olarak güvendikleri sosyal medya üzerinde ürün ve hizmetler hakkında konuşarak elektronik ağızdan ağıza iletişim (E-AAİ) gerçekleştirmektedirler. Tüketiciler sosyal medyada e-AAİ'yi "görüş arama", "görüş iletme" ve "görüş belirtme" olarak üç farklı şekilde yapmaktadırlar. En yaygın davranış görüş aramadır. Çalışmada görüş aramayı motive eden faktörlerin yapay sinir ağları yöntemiyle tahminlemesi amaçlanmıştır. Veriler kabul edilen 512 anketten oluşmaktadır. Motivasyon faktörlerinin yapay sinir ağları ve Bayesian Regülasyon geri yayma ile analizi yapılmıştır. Analizler neticesinde performans değerlerinin kabul 1-Dr. İbrahim TOPAL, ibrahimtopal30@gmail.com, İstanbul/Türkiye, https://orcid.org/0000-0002-7119-9470 184 İbrahim TOPAL edilebilir değerde ve başarı oranının %90 olduğu görülmüştür. Tüketicileri görüş aramaya motive eden faktörlerin belirlenmesi işletmelerin pazarlama amaçlarına ulaşılmasına önemli katkı sağlayabilir. Bu nedenle E-AAİ, literatürde çeşitli motivasyon faktörleri ve analiz yöntemleriyle ele alınmıştır. Bu çalışma motivasyon faktörlerini bir araya getirmesi, e-AAİ davranışını detaylandırması ve yapay sinir ağlarını kullanması ile diğerlerinden farklılaşmaktadır.

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“…Smart tourism integrates tourism resources with information technologies to design intelligent services to provide valuable outcomes to tourists and tourism-related industries. The development of smart tourism is particularly embodied in four main aspects, namely tourism experience, tourism management, tourism service, and tourism marketing [16][17][18][19]. The tracking and recording activity of space-time paths of individual tourists is inserted in this big wave of tourism mining, not as an ultimate purpose, but as a mean of providing valuable knowledge of tourists' mobility and travel behaviors.…”

Section: Introductionmentioning

confidence: 99%

LSTM-Based Deep Learning Model for Predicting Individual Mobility Traces of Short-Term Foreign Tourists

Crivellari

Beinat

2020

Sustainability

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The increasing availability of trajectory recordings has led to the mining of a massive amount of historical track data, allowing for a better understanding of travel behaviors by revealing meaningful motion patterns. In the context of human mobility analysis, the problem of motion prediction assumes a central role and is beneficial for a wide range of applications, including for touristic purposes, such as personalized services or targeted recommendations, and sustainability studies related to crowd management and resource redistribution. This paper tackles a particular case of the trajectory prediction problem, focusing on large-scale mobility traces of short-term foreign tourists. These sparse trajectories, short and non-repetitive, lack spatial and temporal regularity, making prediction analysis based on individual historical motion data unreliable. To face this issue, we hereby propose a deep learning-based approach, taking into account the collective mobility of tourists over the territory. The underlying semantics of motion patterns are captured by means of a long short-term memory (LSTM) neural network model trained on pre-processed location sequences, aiming to predict the next visited place in the trajectory. We tested the methodology on a real-world big dataset, demonstrating its higher feasibility with respect to traditional approaches.

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Forecasting the success of a new tourism service by a neuro-fuzzy technique

Cited by 36 publications

References 35 publications

Hybrid Artificial Intelligence Based Automatic Determination of Travel Preferences of Chinese Tourists

Hybrid Artificial Intelligence Based Automatic Determination of Travel Preferences of Chinese Tourists

Estimation With Artificial Neural Network on Electronic Word of Mouth: Opinion Searching

LSTM-Based Deep Learning Model for Predicting Individual Mobility Traces of Short-Term Foreign Tourists

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