ÇİFT-DUYGU AYRIŞTIRMA VE BULANIK RİSK ÖNCELİKLENDİRME (DSD-FRP): E-TİCARETTE TÜKETİCİ KARAR VERME BELİRSİZLİĞİNİN MODELLENMESİ

Abstract

E-ticarette tüketici karar verme süreci doğası gereği belirsizdir ve çelişkili bilgiler, farklı kullanıcı deneyimleri ve hızla değişen dijital ortamlar tarafından şekillenir. Geleneksel pazarlama araştırmaları, algılanan riski ölçmek için anketlere dayanmış, ancak bu yöntemler maliyetli, yavaş ve ölçek açısından sınırlı kalmıştır. Bu çalışma, 551 binden fazla Amazon değerlendirmesine uygulanan anket gerektirmeyen bir çerçeve önermektedir: Çift-Duygu Ayrıştırma ve Bulanık Risk Önceliklendirme (DSD-FRP). Yöntem, tüketici yorumlarını iyimser ve kötümser sinyallere ayırarak aynı geri bildirim içindeki çelişkileri temsil eder. Bu sinyaller, belirsizliği yakalamak için Hata Türleri ve Etkileri Analizi’nin (FMEA) bulanık uzantılarından esinlenilerek şiddet, oluşma ve tespit edilebilirlik skorlarına dönüştürülür. Ampirik bulgular, müşteri hizmetleri ve marka güveninin en yüksek riskli boyutlar olduğunu, kalite ve fiyatın ise daha düşük risk skorları gösterdiğini ortaya koymaktadır. Bulanık modelin, keskin analizlere kıyasla öncelikleri değiştirmesi, belirsizliği açıkça modellemenin yönetsel sonuçları dönüştürdüğünü göstermektedir. Çalışma, bulanık risk önceliklendirmeyi tüketici karar verme bağlamına genişleterek literatüre katkı yapmakta ve çift-duygu modellemenin kullanıcı tarafından üretilen içerikteki değerini göstermektedir. Uygulayıcılar için sonuçlar, hizmet kalitesi ve marka güvenine yapılacak yatırımların, yalnızca fiyat rekabetinden daha etkili olabileceğini göstermektedir.

Keywords

• Tüketici Karar Verme
• E-ticaret
• Bulanık Mantık
• Risk Önceliklendirme
• Kullanıcı Tarafından Üretilen İçerik
• Çift-Duygu

DUAL-SENTIMENT DECOMPOSITION AND FUZZY RISK PRIORITIZATION (DSD–FRP): MODELING CONSUMER DECISIONMAKING UNCERTAINTY IN E-COMMERCE

Abstract

Consumer decision-making in e-commerce is inherently uncertain, shaped by contradictory information, heterogeneous experiences, and rapidly changing environments. Traditional marketing research has relied on survey-based methods to capture perceived risk, but such approaches are costly, slow, and limited in scale. This study proposes a survey-free framework—Dual-Sentiment Decomposition and Fuzzy Risk Prioritization (DSD-FRP)— applied to over 551,000 Amazon reviews. The method decomposes reviews into optimistic and pessimistic signals, enabling representation of contradictions within the same feedback. These signals are then mapped into fuzzy severity, occurrence, and detectability scores, inspired by fuzzy extensions of Failure Mode and Effects Analysis (FMEA). Empirical findings show that service and brand trust represent the highest-risk decision dimensions, while quality and price— though most frequently mentioned—exhibit lower risk scores. Notably, the fuzzy model reordered priorities compared to crisp analysis, underscoring that explicitly modeling uncertainty alters managerial conclusions. This study contributes to marketing literature by extending fuzzy risk prioritization into consumer decision-making contexts and by demonstrating the value of dual-sentiment modeling in user-generated content. For practitioners, results suggest that investments in service quality and brand authenticity may reduce decision uncertainty more effectively than price competition alone.

Keywords

• Consumer Decision-Making
• E-commerce
• Fuzzy Logic
• Risk Prioritization
• User- Generated Content
• Dual Sentiment

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