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

Günümüz tüketicileri sosyal medyayı güvenilir bir bilgi kaynağı olarak görmekte ve bu platformda 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. Bu davranışlardan en yaygın olanı ise görüş aramadır. 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-aai davranışını detaylandırması ve yapay sinir ağlarını kullanması ile diğerlerinden farklılaşmaktadır. Çalışma en yaygın kullanıma sahip sosyal medya sitesi Facebook temelinde yapılmıştır. Motivasyon faktörleri yapay sinir ağları yöntemiyle tahmin edilmeye çalışılmıştır. Bayesian regülasyon geri yayma ile analizi yapılmıştır. Analizler neticesinde performans değerlerinin kabul edilebilir değerde ve başarı oranının %90 olduğu görülmüştür.

Anahtar Kelimeler:

Elektronik Ağızdan Ağıza İletişim, Motivasyon Faktörleri, Sosyal Ağlar, Yapay Sinir Ağları, Bayesian Regülasyon

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

Today's consumers see social media as a reliable source of information and make electronic word-of-mouth (e-WOM) on this platform by talking about products and services. In social media, e-WOM is used in three different ways: “opinions searching”(being the most common), “opinion giving”, and “opinion forwarding”. 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 motivation factors and analysis methods. This study differs from other research by combining motivation factors and detailing e-WOM behavior as well as using artificial neural networks. Facebook, most widely used social media site, was used for this study. Motivation factors were estimated by artificial neural network method. Bayesian Regulation method was used for the analysis. As a result showed that the performance values were acceptable and the success rate was 90%.

Keywords:

Electronic Word-of-Mouth, Motivation Factors, Social Networks, Artificial Neural Networks, Bayesian Regulation,

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